{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning Nano Degree\n",
    "## Gender Recognition by Voice\n",
    "## Data Preprocess"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "The dataset comes from [Kaggle](https://www.kaggle.com/primaryobjects/voicegender). The origin wave file have been extracted features by R packages, seewave and tuneR. There are 3168 samples with label of gender."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Familar with Data\n",
    "Load the necessary Python libraries and read data of features. The `label` at the last column is what need to be predicted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import pickle\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"voice.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>3163</th>\n",
       "      <th>3164</th>\n",
       "      <th>3165</th>\n",
       "      <th>3166</th>\n",
       "      <th>3167</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>meanfreq</th>\n",
       "      <td>0.059781</td>\n",
       "      <td>0.0660087</td>\n",
       "      <td>0.0773155</td>\n",
       "      <td>0.151228</td>\n",
       "      <td>0.13512</td>\n",
       "      <td>0.131884</td>\n",
       "      <td>0.116221</td>\n",
       "      <td>0.142056</td>\n",
       "      <td>0.143659</td>\n",
       "      <td>0.165509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sd</th>\n",
       "      <td>0.0642413</td>\n",
       "      <td>0.06731</td>\n",
       "      <td>0.0838294</td>\n",
       "      <td>0.0721106</td>\n",
       "      <td>0.0791461</td>\n",
       "      <td>0.0847341</td>\n",
       "      <td>0.0892211</td>\n",
       "      <td>0.0957984</td>\n",
       "      <td>0.0906283</td>\n",
       "      <td>0.0928835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>0.0320269</td>\n",
       "      <td>0.0402287</td>\n",
       "      <td>0.0367185</td>\n",
       "      <td>0.158011</td>\n",
       "      <td>0.124656</td>\n",
       "      <td>0.153707</td>\n",
       "      <td>0.076758</td>\n",
       "      <td>0.183731</td>\n",
       "      <td>0.184976</td>\n",
       "      <td>0.183044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q25</th>\n",
       "      <td>0.0150715</td>\n",
       "      <td>0.0194139</td>\n",
       "      <td>0.00870106</td>\n",
       "      <td>0.0965817</td>\n",
       "      <td>0.0787202</td>\n",
       "      <td>0.0492849</td>\n",
       "      <td>0.0427175</td>\n",
       "      <td>0.0334239</td>\n",
       "      <td>0.0435081</td>\n",
       "      <td>0.0700715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q75</th>\n",
       "      <td>0.0901934</td>\n",
       "      <td>0.0926662</td>\n",
       "      <td>0.131908</td>\n",
       "      <td>0.207955</td>\n",
       "      <td>0.206045</td>\n",
       "      <td>0.201144</td>\n",
       "      <td>0.204911</td>\n",
       "      <td>0.22436</td>\n",
       "      <td>0.219943</td>\n",
       "      <td>0.250827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IQR</th>\n",
       "      <td>0.075122</td>\n",
       "      <td>0.0732523</td>\n",
       "      <td>0.123207</td>\n",
       "      <td>0.111374</td>\n",
       "      <td>0.127325</td>\n",
       "      <td>0.151859</td>\n",
       "      <td>0.162193</td>\n",
       "      <td>0.190936</td>\n",
       "      <td>0.176435</td>\n",
       "      <td>0.180756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skew</th>\n",
       "      <td>12.8635</td>\n",
       "      <td>22.4233</td>\n",
       "      <td>30.7572</td>\n",
       "      <td>1.23283</td>\n",
       "      <td>1.10117</td>\n",
       "      <td>1.76213</td>\n",
       "      <td>0.69373</td>\n",
       "      <td>1.8765</td>\n",
       "      <td>1.59106</td>\n",
       "      <td>1.70503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurt</th>\n",
       "      <td>274.403</td>\n",
       "      <td>634.614</td>\n",
       "      <td>1024.93</td>\n",
       "      <td>4.1773</td>\n",
       "      <td>4.33371</td>\n",
       "      <td>6.63038</td>\n",
       "      <td>2.50395</td>\n",
       "      <td>6.60451</td>\n",
       "      <td>5.3883</td>\n",
       "      <td>5.76912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sp.ent</th>\n",
       "      <td>0.893369</td>\n",
       "      <td>0.892193</td>\n",
       "      <td>0.846389</td>\n",
       "      <td>0.963322</td>\n",
       "      <td>0.971955</td>\n",
       "      <td>0.962934</td>\n",
       "      <td>0.960716</td>\n",
       "      <td>0.946854</td>\n",
       "      <td>0.950436</td>\n",
       "      <td>0.938829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sfm</th>\n",
       "      <td>0.491918</td>\n",
       "      <td>0.513724</td>\n",
       "      <td>0.478905</td>\n",
       "      <td>0.727232</td>\n",
       "      <td>0.783568</td>\n",
       "      <td>0.763182</td>\n",
       "      <td>0.70957</td>\n",
       "      <td>0.654196</td>\n",
       "      <td>0.67547</td>\n",
       "      <td>0.601529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mode</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0838782</td>\n",
       "      <td>0.104261</td>\n",
       "      <td>0.200836</td>\n",
       "      <td>0.013683</td>\n",
       "      <td>0.00800572</td>\n",
       "      <td>0.212202</td>\n",
       "      <td>0.267702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid</th>\n",
       "      <td>0.059781</td>\n",
       "      <td>0.0660087</td>\n",
       "      <td>0.0773155</td>\n",
       "      <td>0.151228</td>\n",
       "      <td>0.13512</td>\n",
       "      <td>0.131884</td>\n",
       "      <td>0.116221</td>\n",
       "      <td>0.142056</td>\n",
       "      <td>0.143659</td>\n",
       "      <td>0.165509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meanfun</th>\n",
       "      <td>0.0842791</td>\n",
       "      <td>0.107937</td>\n",
       "      <td>0.0987063</td>\n",
       "      <td>0.0889648</td>\n",
       "      <td>0.106398</td>\n",
       "      <td>0.18279</td>\n",
       "      <td>0.18898</td>\n",
       "      <td>0.209918</td>\n",
       "      <td>0.172375</td>\n",
       "      <td>0.185607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minfun</th>\n",
       "      <td>0.0157017</td>\n",
       "      <td>0.0158259</td>\n",
       "      <td>0.0156556</td>\n",
       "      <td>0.0177976</td>\n",
       "      <td>0.0169312</td>\n",
       "      <td>0.0837696</td>\n",
       "      <td>0.0344086</td>\n",
       "      <td>0.0395062</td>\n",
       "      <td>0.0344828</td>\n",
       "      <td>0.0622568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxfun</th>\n",
       "      <td>0.275862</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.271186</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.262295</td>\n",
       "      <td>0.275862</td>\n",
       "      <td>0.275862</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.271186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meandom</th>\n",
       "      <td>0.0078125</td>\n",
       "      <td>0.00901442</td>\n",
       "      <td>0.00799006</td>\n",
       "      <td>0.201497</td>\n",
       "      <td>0.712812</td>\n",
       "      <td>0.832899</td>\n",
       "      <td>0.909856</td>\n",
       "      <td>0.494271</td>\n",
       "      <td>0.79136</td>\n",
       "      <td>0.227022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mindom</th>\n",
       "      <td>0.0078125</td>\n",
       "      <td>0.0078125</td>\n",
       "      <td>0.0078125</td>\n",
       "      <td>0.0078125</td>\n",
       "      <td>0.0078125</td>\n",
       "      <td>0.0078125</td>\n",
       "      <td>0.0390625</td>\n",
       "      <td>0.0078125</td>\n",
       "      <td>0.0078125</td>\n",
       "      <td>0.0078125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxdom</th>\n",
       "      <td>0.0078125</td>\n",
       "      <td>0.0546875</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.5625</td>\n",
       "      <td>5.48438</td>\n",
       "      <td>4.21094</td>\n",
       "      <td>3.67969</td>\n",
       "      <td>2.9375</td>\n",
       "      <td>3.59375</td>\n",
       "      <td>0.554688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dfrange</th>\n",
       "      <td>0</td>\n",
       "      <td>0.046875</td>\n",
       "      <td>0.0078125</td>\n",
       "      <td>0.554688</td>\n",
       "      <td>5.47656</td>\n",
       "      <td>4.20312</td>\n",
       "      <td>3.64062</td>\n",
       "      <td>2.92969</td>\n",
       "      <td>3.58594</td>\n",
       "      <td>0.546875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modindx</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0526316</td>\n",
       "      <td>0.0465116</td>\n",
       "      <td>0.247119</td>\n",
       "      <td>0.208274</td>\n",
       "      <td>0.161929</td>\n",
       "      <td>0.277897</td>\n",
       "      <td>0.194759</td>\n",
       "      <td>0.311002</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <td>male</td>\n",
       "      <td>male</td>\n",
       "      <td>male</td>\n",
       "      <td>male</td>\n",
       "      <td>male</td>\n",
       "      <td>female</td>\n",
       "      <td>female</td>\n",
       "      <td>female</td>\n",
       "      <td>female</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               0           1           2          3          4          3163  \\\n",
       "meanfreq   0.059781   0.0660087   0.0773155   0.151228    0.13512   0.131884   \n",
       "sd        0.0642413     0.06731   0.0838294  0.0721106  0.0791461  0.0847341   \n",
       "median    0.0320269   0.0402287   0.0367185   0.158011   0.124656   0.153707   \n",
       "Q25       0.0150715   0.0194139  0.00870106  0.0965817  0.0787202  0.0492849   \n",
       "Q75       0.0901934   0.0926662    0.131908   0.207955   0.206045   0.201144   \n",
       "IQR        0.075122   0.0732523    0.123207   0.111374   0.127325   0.151859   \n",
       "skew        12.8635     22.4233     30.7572    1.23283    1.10117    1.76213   \n",
       "kurt        274.403     634.614     1024.93     4.1773    4.33371    6.63038   \n",
       "sp.ent     0.893369    0.892193    0.846389   0.963322   0.971955   0.962934   \n",
       "sfm        0.491918    0.513724    0.478905   0.727232   0.783568   0.763182   \n",
       "mode              0           0           0  0.0838782   0.104261   0.200836   \n",
       "centroid   0.059781   0.0660087   0.0773155   0.151228    0.13512   0.131884   \n",
       "meanfun   0.0842791    0.107937   0.0987063  0.0889648   0.106398    0.18279   \n",
       "minfun    0.0157017   0.0158259   0.0156556  0.0177976  0.0169312  0.0837696   \n",
       "maxfun     0.275862        0.25    0.271186       0.25   0.266667   0.262295   \n",
       "meandom   0.0078125  0.00901442  0.00799006   0.201497   0.712812   0.832899   \n",
       "mindom    0.0078125   0.0078125   0.0078125  0.0078125  0.0078125  0.0078125   \n",
       "maxdom    0.0078125   0.0546875    0.015625     0.5625    5.48438    4.21094   \n",
       "dfrange           0    0.046875   0.0078125   0.554688    5.47656    4.20312   \n",
       "modindx           0   0.0526316   0.0465116   0.247119   0.208274   0.161929   \n",
       "label          male        male        male       male       male     female   \n",
       "\n",
       "               3164        3165       3166       3167  \n",
       "meanfreq   0.116221    0.142056   0.143659   0.165509  \n",
       "sd        0.0892211   0.0957984  0.0906283  0.0928835  \n",
       "median     0.076758    0.183731   0.184976   0.183044  \n",
       "Q25       0.0427175   0.0334239  0.0435081  0.0700715  \n",
       "Q75        0.204911     0.22436   0.219943   0.250827  \n",
       "IQR        0.162193    0.190936   0.176435   0.180756  \n",
       "skew        0.69373      1.8765    1.59106    1.70503  \n",
       "kurt        2.50395     6.60451     5.3883    5.76912  \n",
       "sp.ent     0.960716    0.946854   0.950436   0.938829  \n",
       "sfm         0.70957    0.654196    0.67547   0.601529  \n",
       "mode       0.013683  0.00800572   0.212202   0.267702  \n",
       "centroid   0.116221    0.142056   0.143659   0.165509  \n",
       "meanfun     0.18898    0.209918   0.172375   0.185607  \n",
       "minfun    0.0344086   0.0395062  0.0344828  0.0622568  \n",
       "maxfun     0.275862    0.275862       0.25   0.271186  \n",
       "meandom    0.909856    0.494271    0.79136   0.227022  \n",
       "mindom    0.0390625   0.0078125  0.0078125  0.0078125  \n",
       "maxdom      3.67969      2.9375    3.59375   0.554688  \n",
       "dfrange     3.64062     2.92969    3.58594   0.546875  \n",
       "modindx    0.277897    0.194759   0.311002       0.35  \n",
       "label        female      female     female     female  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.T[[0, 1, 2, 3, 4, 3163, 3164, 3165, 3166, 3167]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of Label\n",
    "There are 3168 samples. The first half is all about male, and the last half is all about female. To make the test set objective, the dataset need to be shuffled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>meanfreq</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.180907</td>\n",
       "      <td>0.029918</td>\n",
       "      <td>0.039363</td>\n",
       "      <td>0.163662</td>\n",
       "      <td>0.184838</td>\n",
       "      <td>0.199146</td>\n",
       "      <td>0.251124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sd</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.057126</td>\n",
       "      <td>0.016652</td>\n",
       "      <td>0.018363</td>\n",
       "      <td>0.041954</td>\n",
       "      <td>0.059155</td>\n",
       "      <td>0.067020</td>\n",
       "      <td>0.115273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.185621</td>\n",
       "      <td>0.036360</td>\n",
       "      <td>0.010975</td>\n",
       "      <td>0.169593</td>\n",
       "      <td>0.190032</td>\n",
       "      <td>0.210618</td>\n",
       "      <td>0.261224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q25</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.140456</td>\n",
       "      <td>0.048680</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>0.111087</td>\n",
       "      <td>0.140286</td>\n",
       "      <td>0.175939</td>\n",
       "      <td>0.247347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q75</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.224765</td>\n",
       "      <td>0.023639</td>\n",
       "      <td>0.042946</td>\n",
       "      <td>0.208747</td>\n",
       "      <td>0.225684</td>\n",
       "      <td>0.243660</td>\n",
       "      <td>0.273469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IQR</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.084309</td>\n",
       "      <td>0.042783</td>\n",
       "      <td>0.014558</td>\n",
       "      <td>0.042560</td>\n",
       "      <td>0.094280</td>\n",
       "      <td>0.114175</td>\n",
       "      <td>0.252225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>skew</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>3.140168</td>\n",
       "      <td>4.240529</td>\n",
       "      <td>0.141735</td>\n",
       "      <td>1.649569</td>\n",
       "      <td>2.197101</td>\n",
       "      <td>2.931694</td>\n",
       "      <td>34.725453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kurt</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>36.568461</td>\n",
       "      <td>134.928661</td>\n",
       "      <td>2.068455</td>\n",
       "      <td>5.669547</td>\n",
       "      <td>8.318463</td>\n",
       "      <td>13.648905</td>\n",
       "      <td>1309.612887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sp.ent</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.895127</td>\n",
       "      <td>0.044980</td>\n",
       "      <td>0.738651</td>\n",
       "      <td>0.861811</td>\n",
       "      <td>0.901767</td>\n",
       "      <td>0.928713</td>\n",
       "      <td>0.981997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sfm</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.408216</td>\n",
       "      <td>0.177521</td>\n",
       "      <td>0.036876</td>\n",
       "      <td>0.258041</td>\n",
       "      <td>0.396335</td>\n",
       "      <td>0.533676</td>\n",
       "      <td>0.842936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mode</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.165282</td>\n",
       "      <td>0.077203</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.118016</td>\n",
       "      <td>0.186599</td>\n",
       "      <td>0.221104</td>\n",
       "      <td>0.280000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>centroid</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.180907</td>\n",
       "      <td>0.029918</td>\n",
       "      <td>0.039363</td>\n",
       "      <td>0.163662</td>\n",
       "      <td>0.184838</td>\n",
       "      <td>0.199146</td>\n",
       "      <td>0.251124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meanfun</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.142807</td>\n",
       "      <td>0.032304</td>\n",
       "      <td>0.055565</td>\n",
       "      <td>0.116998</td>\n",
       "      <td>0.140519</td>\n",
       "      <td>0.169581</td>\n",
       "      <td>0.237636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>minfun</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.036802</td>\n",
       "      <td>0.019220</td>\n",
       "      <td>0.009775</td>\n",
       "      <td>0.018223</td>\n",
       "      <td>0.046110</td>\n",
       "      <td>0.047904</td>\n",
       "      <td>0.204082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxfun</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.258842</td>\n",
       "      <td>0.030077</td>\n",
       "      <td>0.103093</td>\n",
       "      <td>0.253968</td>\n",
       "      <td>0.271186</td>\n",
       "      <td>0.277457</td>\n",
       "      <td>0.279114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>meandom</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.829211</td>\n",
       "      <td>0.525205</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.419828</td>\n",
       "      <td>0.765795</td>\n",
       "      <td>1.177166</td>\n",
       "      <td>2.957682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mindom</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.052647</td>\n",
       "      <td>0.063299</td>\n",
       "      <td>0.004883</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.023438</td>\n",
       "      <td>0.070312</td>\n",
       "      <td>0.458984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxdom</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>5.047277</td>\n",
       "      <td>3.521157</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>2.070312</td>\n",
       "      <td>4.992188</td>\n",
       "      <td>7.007812</td>\n",
       "      <td>21.867188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dfrange</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>4.994630</td>\n",
       "      <td>3.520039</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.044922</td>\n",
       "      <td>4.945312</td>\n",
       "      <td>6.992188</td>\n",
       "      <td>21.843750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>modindx</th>\n",
       "      <td>3168.0</td>\n",
       "      <td>0.173752</td>\n",
       "      <td>0.119454</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.099766</td>\n",
       "      <td>0.139357</td>\n",
       "      <td>0.209183</td>\n",
       "      <td>0.932374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count       mean         std       min       25%       50%  \\\n",
       "meanfreq  3168.0   0.180907    0.029918  0.039363  0.163662  0.184838   \n",
       "sd        3168.0   0.057126    0.016652  0.018363  0.041954  0.059155   \n",
       "median    3168.0   0.185621    0.036360  0.010975  0.169593  0.190032   \n",
       "Q25       3168.0   0.140456    0.048680  0.000229  0.111087  0.140286   \n",
       "Q75       3168.0   0.224765    0.023639  0.042946  0.208747  0.225684   \n",
       "IQR       3168.0   0.084309    0.042783  0.014558  0.042560  0.094280   \n",
       "skew      3168.0   3.140168    4.240529  0.141735  1.649569  2.197101   \n",
       "kurt      3168.0  36.568461  134.928661  2.068455  5.669547  8.318463   \n",
       "sp.ent    3168.0   0.895127    0.044980  0.738651  0.861811  0.901767   \n",
       "sfm       3168.0   0.408216    0.177521  0.036876  0.258041  0.396335   \n",
       "mode      3168.0   0.165282    0.077203  0.000000  0.118016  0.186599   \n",
       "centroid  3168.0   0.180907    0.029918  0.039363  0.163662  0.184838   \n",
       "meanfun   3168.0   0.142807    0.032304  0.055565  0.116998  0.140519   \n",
       "minfun    3168.0   0.036802    0.019220  0.009775  0.018223  0.046110   \n",
       "maxfun    3168.0   0.258842    0.030077  0.103093  0.253968  0.271186   \n",
       "meandom   3168.0   0.829211    0.525205  0.007812  0.419828  0.765795   \n",
       "mindom    3168.0   0.052647    0.063299  0.004883  0.007812  0.023438   \n",
       "maxdom    3168.0   5.047277    3.521157  0.007812  2.070312  4.992188   \n",
       "dfrange   3168.0   4.994630    3.520039  0.000000  2.044922  4.945312   \n",
       "modindx   3168.0   0.173752    0.119454  0.000000  0.099766  0.139357   \n",
       "\n",
       "                75%          max  \n",
       "meanfreq   0.199146     0.251124  \n",
       "sd         0.067020     0.115273  \n",
       "median     0.210618     0.261224  \n",
       "Q25        0.175939     0.247347  \n",
       "Q75        0.243660     0.273469  \n",
       "IQR        0.114175     0.252225  \n",
       "skew       2.931694    34.725453  \n",
       "kurt      13.648905  1309.612887  \n",
       "sp.ent     0.928713     0.981997  \n",
       "sfm        0.533676     0.842936  \n",
       "mode       0.221104     0.280000  \n",
       "centroid   0.199146     0.251124  \n",
       "meanfun    0.169581     0.237636  \n",
       "minfun     0.047904     0.204082  \n",
       "maxfun     0.277457     0.279114  \n",
       "meandom    1.177166     2.957682  \n",
       "mindom     0.070312     0.458984  \n",
       "maxdom     7.007812    21.867188  \n",
       "dfrange    6.992188    21.843750  \n",
       "modindx    0.209183     0.932374  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ranges of features differ a lot. So normalization is needed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of Label\n",
    "There are 3168 samples. The first half is all about male, and the last half is all about female. To make the test set objective, the dataset need to be shuffled."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Feature Engineering\n",
    "\n",
    "- There are no features missing.\n",
    "- The range of every feature differs from each other.\n",
    "- The features are numbers. One-hot encoding is unnecessary.\n",
    "- Label of \"female\" and \"male\" need to be encoded.\n",
    "- By observing, the important features may be found."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split features and label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_raw = data.drop('label', axis = 1)\n",
    "label_raw = data['label']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution of Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jachinshen/Projects/python/Udacity/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2910: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/home/jachinshen/Projects/python/Udacity/lib/python3.6/site-packages/matplotlib/figure.py:459: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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/mZqZkS1Y5X77Vgqd6n5U9Lsn9ZTxjspYZ1EqY1mhhxrPZmZV5wWrrMQ61v3IrN08VZ2ZmZnlKiLOiYjdI2Iy2d2QWyPiZDxtqBWQrzybWUvuW7m27i07d4kya87wboUz91uf62DBgnH3IyscN57NzMysMNz9yIrO3TbMzMzMzJrkxrOZmZmZWZPceDYzMzMza5Ibz2ZmZmZmTfKAQTMzMzPrqsmzbqw7s0wZZmpy49nMzMzMNqvRas29xo1nM+sZI1X8ZbjaYWZm+XPj2cw6op1XKHy1w8zMisIDBs3MzMzMmuTGs/U0SZMk3SbpQUkPSPp4St9Z0kJJD6d/d0rpknSxpKWS7pV0YL4lMDMzs25y49l63XpgZkTsCxwMnC5pX2AWcEtE7APckrYBjgL2SY8ZwKXdz7KZmZnlxX2eradFxGPAY+n5s5KWABOBqUB/OmwuMACcndKvjIgA7pA0XtJu6TxmZmal53EmI/OVZ7NE0mTgAGAR0FfTIH4c6EvPJwLLa162IqWZmZlZD/CVZzNA0vbAd4C/jYhnJG3YFxEhKUZ5vhlk3Tro6+tjYGBgk2MGBwfrppdN37Ywc7/1eWejZQMDA5X5TszMrHPceLaeJ2lLsobzvIj4bkp+Yqg7hqTdgFUpfSUwqeblu6e0jUTEHGAOwJQpU6K/v3+T9x0YGKBeetlcMu96Pndf+auSZSf3V+Y7MTOzznG3Detpyi4xXwYsiYjP1+xaAExLz6cB19ekn5Jm3TgYWOv+zmZmZr2j/JeLzFrzNuDDwH2S7klpnwRmA9dImg48ChyX9t0EHA0sBZ4DTutuds3MzCxPPd949nK9vS0ibgfUYPehdY4P4PSOZsrMzMwKy902zMzMzMyaNObGs1dmMzMzM7Ne00q3jaGV2X4maQdgsaSFwKlkK7PNljSLbGW2s9l4ZbaDyFZmO6iVzJtZ9zTq4jRzvy5nxMzMLEdjbjx7ZbZNeUUeMzMzs2prS59nr8xmZmZmZr2g5dk28liZbSTtXOmsne892lXYLpl3fd30/SbuOKo8dZJXY7MqmTzrRmbut55Th91B8qw7ZmZWq6XGc14rs41k+H98rVh2cvvee+Z+69uyCtto89RJXo3NrBgkTQKuJLvTF8CciLhI0s7A1cBkYBlwXEQ8nRYHuohszvLngFMj4md55N3MrGzG3JprYmW22Wy6MtsZkuaTDRRsaWU29y82M9vAA7it1PwD0MqklUuhXpnNzKwAPIDbKsA/AA1ofHG0SF3oWpltwyuzmZkVTIsDuDdqPLc6BqXTqjLuYvh4mNGOkemEbn+u/gFoZdLzy3ObmVVFuwdwtzoGpdOqMu5i+HiZdo2RaUWe42t66QdgPd38UXjfyrV107s1f/9ofigW6btz49nMrAI6MYDbrNt67QdgPd38UdjOSRbGYjQ/FIs0YUJb5nk2M7P8NDGAGzYdwH2KMgfT4gBus3YY6Qdg2u8fgFYIbjybmZXf0ADuQyTdkx5Hkw3gPkzSw8C70zZkA7gfIRvA/TXgb3LIs9kG/gFoZeJuG2ZmJecB3FYBnsHLSsONZzMzM8uVfwBambjbhvU0SZdLWiXp/pq0nSUtlPRw+nenlC5JF0taKuleSQfml3MzMzPLg688W6+7Avgi2cpWQ2bhSfktKcOE/WZm1j2+8mw9LSJ+BKweljyVbDJ+0r/vr0m/MjJ3AOOHRoGbmZlZb3Dj2WxTo52U38zMzHqEu22MQaPbuFY9Y5mUH5pb1apsSws3WgWqCEsJt0tZV7syM7PucePZbFMtr8rWzKpWZVtauNFKVEVYSrhdyrralRWfL7pYnqoQf0Uaf1KN//E6pArBZmMyNCn/bDadlP8MSfPJBgp6Un4zM7Me48az9TRJVwH9wARJK4Bz8aT8ZmZmpTDShc5OXZV249l6WkSc2GCXJ+U3MzOzTbjxbFZhReojVjX+bM3MepOnqjMzMzMza5KvPJtVgAe3mpmZdYcbzyXjW8VmZmZm+XHj2czMzKzkfAeye9x4rghfkTYzMzPrPDeezWwjvnphZmbWmBvPFecr0tXSroatG8hmZmZj46nqzMzMzMya5CvPPSqP5SzNzKwcfNeyuHznMH++8mxmZmZm1iRfeTbLyX0r13KqryCYmZmVihvPZmZmbeJb6mbV58azmZmZWcH4h1hxuc+zmZmZmVmT3Hg2MzMzM2tS17ttSDoSuAjYAvh6RMzudh5sZJ6iaGSOYasCx3FrfEu9GKoQx8NjaeZ+6z2YvOC62niWtAXwJeAwYAVwl6QFEfFgN/NhNlaOYducMsyh7ji2KihTHPvHVj46dTGw21ee3wIsjYhHACTNB6YChQt0y0cJrno7hq0KejKOx/LDxo2eQuvJOLb8dbvxPBFYXrO9Ajioy3mwMRrtraWK/mfkGLYqKFUcN6ozrjhyu46/hxVa2+LY37+NRuGmqpM0A5iRNgclPZRnftrlYzABeDLvfLTT5sqkC9r3XiOca8/2vUv7NBnHlYiJKsV2p8tS0TjOzbsuqE7s1Sry35RjuPOK/P23W15lbTWOu914XglMqtnePaVtEBFzgDndzFQ3SLo7IqbknY92qmKZmrDZGIbm4rgqn19VygHVKstmtC2O81TV76uq5eqASrYpeun7L2tZuz1V3V3APpL2krQVcAKwoMt5MGuFY9iqwHFsVeA4tlx09cpzRKyXdAZwM9m0MpdHxAPdzINZKxzDVgWOY6sCx7Hlpet9niPiJuCmbr9vAZTqtlGTqlimzWpjDFfl86tKOaBaZRlRReriqn5fVS1X21Ukjofrpe+/lGVVROSdBzMzMzOzUvDy3GZmZmZmTXLjuc0kHSnpIUlLJc2qs/8dkn4mab2kY/PI42g0UZ6zJD0o6V5Jt0gq5HRFeWriM9xa0tVp/yJJk7ufy82rSmw7psulKnE3nOPQqhrb9VQu3iPCjzY9yAYs/Bp4LbAV8Atg32HHTAb+GLgSODbvPLehPO8CXpme/zVwdd75LtKjyc/wb4CvpOcnFPEzrEpsO6bL9ahK3I2xXI7DCj+qGtstlLVU8e4rz+21YanQiHgRGFoqdIOIWBYR9wK/zyODo9RMeW6LiOfS5h1k82zayzb7Gabtuen5tcChktTFPDajKrHtmC6XqsTdcI5Dq2ps11O5eHfjub3qLRU6Mae8tMNoyzMd+F5Hc1Q+zXyGG46JiPXAWmCXruSueVWJbcd0uVQl7oZzHFpVY7ueysV74ZbntnKS9OfAFOCdeefFrB0c01YEjkPrJWWJdzee26upJW9LpKnySHo38A/AOyPihS7lrSya+QyHjlkhaRywI/BUd7LXtKrEtmO6XKoSd8M5Dq2qsV1P5eLd3Tbaq2pLhW62PJIOAL4KvC8iVuWQx6JrJiYWANPS82OBWyONmiiQqsS2Y7pcqhJ3wzkOraqxXU/14j3vEYtVewBHA78iG1n6Dynt02QBAfAnZP191pFdXXwg7zy3WJ4fAk8A96THgrzzXLRHE5/hNsC3gaXAncBr887zGMtRith2TJfrUZW4G0O5HIcVf1Q1tsdY1lLFu1cYNDMzMzNrkrttdImkKyR9Nj3/a0lPSBqUVLRZFcxGTdKy1F/NrCdJ6pe0Iu98mI2VMt+Q9LSkO/POT5G58dxlkrYEPg8cHhHbR0TRBoaZ5cYNEDOz3LwdOAzYPSLekndmisyN5+7rI+vj+kC9nWm2BbOe49g3M8vVnsCyiFiXd0aKzo3nDpF0QFqT/llJV5M1mF8LPJQOWSPp1nRsSDpd0sPAwyntIknLJT0jabGkP6s593mSrpF0ZTr/A5Km1Ow/UNLP075vS7p6qMtI2v8eSfdIWiPpPyX9cRc+EusRkt4g6b8knZhie++afbXdl/olrZB0tqTHgavIJsZ/TerSNCjpNTkVw0osdSP6e0n3Slon6TJJfZK+l+rFH0raKR37bUmPS1or6UeS3pjSt0r15JlpewtJP5H0z2l72xTPT0t6kGxwV20e3iBpINWzD0h6X82+KyR9OeVnMJ331ZK+kM73yzT7gBnQ+ZiWNB34OvDWFJOfknSqpNuH5WNDnZ7i+EuSbkx5WCTpD7v7yeTDjecOUDYVy/8DvgnsTDaTwgeBR4A3psPGR8QhNS97P3AQsG/avgvYP73+34BvS9qm5vj3kS1xOZ5sypcv1rz3dcAV6bVXAf+7Jm8HAJcDf0W2it1XgQWStm695NbrJB0I3AycGRFXNfGSV5PF6Z7AKcBRwG9Sl6btI+I3ncutVdwHyW5B/xHwXrIfZp8EdiX7v+9j6bjvAfsAfwD8DJgHENkywn8OfFrSG4BZwBbA+el15wJ/mB5H8PJ0k0Pd8/4d+EE675nAPEmvq8nfccA/AhOAF4CfpvefAFxL1r3PrFbHYjoiLgM+Cvw01b3nNpmnE4BPATuRzRh1/siHV4Mbz51xMLAl8IWI+F1EXEvWGB7Jv0bE6oj4H4CI+FZEPBUR6yPic8DWQG3Fe3tE3BQRL5E10t9U897jgIvTe3+XbPqzITOAr0bEooh4KSLmklXcB7dYZrM/I/shd0pE3NDka34PnBsRLwzFvlmbXBIRT0TESuDHwKKI+HlEPE92geEAgIi4PCKejWxRhvOAN0naMe27H/gs2cWQvwM+nOpcyBq/56d6ezlwcc17HwxsD8yOiBcj4lbgBuDEmmOui4jFNfl5PiKuTOe/eih/ZjU6HdNjcV1E3BkR68ka6fu3cK7ScOO5M14DrIyN5wF8dDOvqV33HUl/J2lJuu2yhmzVuQk1hzxe8/w5YBtlfUbrvXftufcEZqZbiWvSuSel15m14qPAf0bEwChe89tU8Zu12xM1z/+nzvb26bb1bEm/lvQMsCztr61r55LVmzdFxMM16a9h47r10eH7IuL3w/ZPHE3+GhXMelanY3oshrdFeiJu3XjujMeAiZJUk7bHZl6zobGrrH/zJ8iubOwUEeOBtYAavHZz7127LOZysqsl42ser2zyFrvZSD4K7CHpwpq054BX1my/ethrhk8074nnrZtOAqYC7ya7QDE5pdfWn18mu2p8hKS316Q/xsZ1a20d/xtgkqRXDNtf1eWXrThaienh1lFTf0saXn/3LDeeO+OnwHrgY5K2lPQBYDTTvuyQXv9bYFwaoPKqUbz3S8AZksZJmjrsvb8GfFTSQcpsJ+kYSTuMIn9m9TwLHAm8Q9LslHYPcFK6GnIk8M7NnOMJYJehW4xmHbYDWbe1p8gaCf9Su1PSh4E3A6eS9SedK2noyto1wDmSdpK0O1m/5iGLyH44fiL9H9BP1kd1fueKYga0FtPD/QJ4o6T905ir8zqU59Jx47kDUqf8D5AF52rgeOC7ozjFzcD3yZayfBR4nmHdOpp47+nAGrLBATeQ/TEREXcDf0k2wPBpsg7+p44ib2YNRcQasgEtR0n6DPBxskbDGuBksn52I73+l2SDXB9J3Yrcncg66UqyOnYl8CBwx9AOSXsAXyDrwz8YEf8G3A0M3Vn5VHrtf5ENDPzm0GtTPfxesgGwT5Jd6TslxbdZJ7US0xuJiF+RLaH9Q7KZwG6vd1wv8vLcPUDSIuArEfGNvPNiZmZmVma+8lxBkt6Z5gwdJ2ka8MdkV7LNzMzMrAVe0auaXkfWH287srmlj42Ix/LNkpmZmVn5uduGmZmZmVmT3G3DzMzMzKxJhe62MWHChJg8eXLdfevWrWO77bbrboa6rOplbEf5Fi9e/GRE7NqmLHVEozgu+/db5vwXLe+O42JzGTevjDFctO+1SPnp1bw0HccRUdjHm9/85mjktttua7ivKqpexnaUD7g7ChCrIz0axXHZv98y579oeXccF5vLuHlljOGifa9Fyk+v5qXZOHa3DesJkraRdKekX0h6QNKnUvpekhZJWirpaklbpfSt0/bStH9yzbnOSekPSToinxKZmZlZHtx4tl7xAnBIRLwJ2B84UtLBwAXAhRGxN9miMdPT8dOBp1P6hek4JO0LnAC8kWw1vS9L2qKrJTEzM7PcuPFsPSHdkRlMm1umRwCHANem9LnA+9PzqWmbtP9QSUrp8yPihYj4L7IVGkez9LqZmZmVWKEHDI7kvpVrOXXWjZukL5t9TA4d9BiZAAAgAElEQVS5sTJIV4gXA3sDXwJ+DayJiPXpkBXAxPR8ImlJ9IhYL2ktsEtKv6PmtLWvqX2vGcAMgL6+PgYGBjbJz6rVa7lk3vV187rfxB1HV7gcDA4O1i1XGZQ570XjutjMimpynboJWq+fStt4NhutiHgJ2F/SeOA64PUdfK85wByAKVOmRH9//ybHXDLvej53X/0/wWUnb3p80QwMDFCvXGVQ5rybmVm+3G3Dek5ErAFuA94KjJc01ILdHViZnq8EJgGk/TsCT9Wm13mNmZmZVZwbz9YTJO2arjgjaVvgMGAJWSP62HTYNGCoH8WCtE3af2uaxmYBcEKajWMvYB/gzu6UwszMzPLmbhvWK3YD5qZ+z68AromIGyQ9CMyX9Fng58Bl6fjLgG9KWgqsJpthg4h4QNI1wIPAeuD01B3EzMzMeoAbz9YTIuJe4IA66Y9QZ7aMiHge+FCDc50PnN/uPJqZmVnxuduGmZmZmVmT3Hg2MzMzM2uSG89mZmZmZk1y49nMzMzMrEluPJuZmZmZNcmNZzMzMzOzJrnxbGZWAZLGS7pW0i8lLZH0Vkk7S1oo6eH0707pWEm6WNJSSfdKOjDv/JuZlUVLjWdX1mZmhXER8P2IeD3wJrIVNGcBt0TEPsAtaRvgKLLVMfcBZgCXdj+71oskXS5plaT7a9LOk7RS0j3pcXTNvnNSu+EhSUfUpB+Z0pZKmjX8fcw6qdUrz66szcxyJmlH4B2kFTIj4sWIWANMBeamw+YC70/PpwJXRuYOYLyk3bqcbetNVwBH1km/MCL2T4+bACTtS7a66xvTa74saYu0UuyXyNoV+wInpmPNumLMKwzWVNanQlZZAy9Kmgr0p8PmAgPA2dRU1sAd6ar1bhHx2Jhzb2ZmAHsBvwW+IelNwGLg40BfTR37ONCXnk8Elte8fkVK26g+ljSD7GIHfX19DAwMbPLGfdvCzP3Wb5Je79iyGhwcrFR56ulWGSPiR5ImN3n4VGB+RLwA/Jekpby8IuzStEIskuanYx9sc3bN6mplee7cKmtwhV0FVS+fWReNAw4EzoyIRZIu4uW7fgBEREiK0Zw0IuYAcwCmTJkS/f39mxxzybzr+dx9m/5XsuzkTY8tq4GBAeqVvUoKUMYzJJ0C3A3MjIinydoId9QcM9RugE3bEwfVO+lIbYqi/R9UpPxUJS/12onQeluxlcZzbpU1uMKugqqXz6yLVgArImJR2r6WrD5+YugOX+qWsSrtXwlMqnn97inNLA+XAp8BIv37OeAj7TjxSG2Kov0fVKT8VCUvp866sW56q23FVvo816usDyRV1gCurM3MOi8iHgeWS3pdSjqU7Bb2AmBaSpsGXJ+eLwBOSQO5DwbWugud5SUinoiIlyLi98DXeLlrRqN2g9sTlqsxN55dWZuZFcqZwDxJ9wL7A/8CzAYOk/Qw8O60DXAT8AiwlKyx8jfdz65ZZthg1f8NDM3EsQA4QdLWkvYim3DgTuAuYB9Je0naimxQ4YJu5tl6WyvdNuDlynorsor4NLIG+TWSpgOPAselY28CjiarrJ9Lx5qZWRtExD3AlDq7Dq1zbACndzxTZsNIuopsUoEJklYA5wL9kvYn67axDPgrgIh4QNI1ZBfm1gOnR8RL6TxnADcDWwCXR8QDXS6K9bCWGs+urM3MzKxZEXFineTLRjj+fOD8Ouk3kV2UM+s6rzBoZmZmZtYkN57NzMzMzJrkxrOZmZmZWZPceDYzMzMza5Ibz2ZmZmZmTXLj2czMzMysSW48m5mZmZk1yY1nMzMzM7MmufFsPUHSJEm3SXpQ0gOSPp7Sd5a0UNLD6d+dUrokXSxpqaR7JR1Yc65p6fiHJU1r9J5mZmZWPW48W69YD8yMiH2Bg4HTJe0LzAJuiYh9gFvSNsBRwD7pMQO4FLLGNtlysgcBbwHOHWpwm5mZWfW58Ww9ISIei4ifpefPAkuAicBUYG46bC7w/vR8KnBlZO4AxkvaDTgCWBgRqyPiaWAhcGQXi2JmZmY5cuPZeo6kycABwCKgLyIeS7seB/rS84nA8pqXrUhpjdLNzMysB4zLOwNm3SRpe+A7wN9GxDOSNuyLiJAUbXqfGWTdPejr62NgYGCTY/q2hZn7ra/7+nrHF83g4GAp8llPmfNuZmb5cuPZeoakLckazvMi4rsp+QlJu0XEY6lbxqqUvhKYVPPy3VPaSqB/WPrA8PeKiDnAHIApU6ZEf3//8EO4ZN71fO6++n+Cy07e9PiiGRgYoF65yqDMeTczs3y524b1BGWXmC8DlkTE52t2LQCGZsyYBlxfk35KmnXjYGBt6t5xM3C4pJ3SQMHDU5qZmZn1AF95tl7xNuDDwH2S7klpnwRmA9dImg48ChyX9t0EHA0sBZ4DTgOIiNWSPgPclY77dESs7k4RzMzMLG9uPFtPiIjbATXYfWid4wM4vcG5Lgcub1/uzMzMrCzcbcPMzMzMrEluPJuZVYCkLST9XNINaXsvSYvSKplXS9oqpW+dtpem/ZPzzLeZWdm03Hh2hW1mVggfJ1v8Z8gFwIURsTfwNDA9pU8Hnk7pF6bjzLpC0uWSVkm6vyZtZ0kLJT2c/t0ppUvSxandcK+kA2teMy0d/7CkafXey6xT2tHneajCflXaHqqw50v6CllFfSk1FbakE9Jxx7fh/VsyedaNDfctm33MqF7T6Hgzs06StDtwDHA+cFaaXeYQ4KR0yFzgPLK6eGp6DnAt8EVJSv38C8l1bqVcAXwRuLImbRZwS0TMljQrbZ8NHAXskx4HkcXvQZJ2Bs4FpgABLJa0IK36atZxLTWee7XCNjMrmC8AnwB2SNu7AGsiYmgVntqVMDeskhkR6yWtTcc/2b3s1jfaOteN6vKJiB/VufM8lZfnz59LNnf+2Sn9ytROuEPS+DQffz+wcGimI0kLgSOBqzqcfTOg9SvPba+wm1mZDRqvzjbaVcMarfA2Fu1esazqq6BVvXxm3SDpPcCqiFgsqb/N5x7zSplj+dtuV33sunj0ci5jX5pHH+BxoC8939BuSIbaFI3SNzFSDBftey1SfqqSl06t4jvmxnOnKuxmVmaDxquzjXZltlPbeHW53avCVX0VtKqXz6xL3ga8T9LRwDZkXeguAsZLGpcuZgytkAkvr565QtI4YEfgqXonbmWlzLHUh+2qj10Xj15RyhgRIaltd6RHiuGilHlIkfJTlbw0qlNarSNaGTA4VGEvA+aTddfYUGGnY+pV2GyuwjYzs+ZExDkRsXtETAZOAG6NiJOB24Bj02HDV88cGmB1bDq+sN3nrCc8kbpjkP5dldI3tBuSoTZFo3Szrhhz49kVtplZoZ1NNhZlKVkXuctS+mXALin9LLLBWWZ5qm0fDG83nJJm3TgYWJu6d9wMHC5ppzQzx+EpzawrOrHC4NnAfEmfBX7OxhX2N1OFvZqswW1mZm0SEQNkg62IiEeAt9Q55nngQ13NmFki6SqyAX8TJK0gmzVjNnCNpOnAo8Bx6fCbgKOBpcBzwGkAEbFa0meAu9Jxnx4aPGjWDW1pPLvCNjMzs82JiBMb7Dq0zrEBnN7gPJcDl7cxa2ZN8wqDZmZmZmZNcuPZzMzMzKxJbjybmZmZmTXJjWczMzMzsya58WxmZmZm1iQ3ns3MzMzMmuTGs5mZmZlZk9x4NjMzMzNrkhvPZmZmZmZNcuPZzMzMzKxJbjybmZmZmTXJjWczMzMzsyaNyzsDZmZm7TR51o1105fNPqbLOTGzKvKVZzMzMzOzJvnKcxv5akdxSboceA+wKiL+V0rbGbgamAwsA46LiKclCbgIOBp4Djg1In6WXjMN+Md02s9GxNxulsPMzMzy5SvP1iuuAI4cljYLuCUi9gFuSdsARwH7pMcM4FLY0Ng+FzgIeAtwrqSdOp5zMzMzKww3nq0nRMSPgNXDkqcCQ1eO5wLvr0m/MjJ3AOMl7QYcASyMiNUR8TSwkE0b5GZmZlZh7rZhvawvIh5Lzx8H+tLzicDymuNWpLRG6ZuQNIPsqjV9fX0MDAxs+ubbwsz91tfNWL3ji2ZwcLAU+aynzHm3sWvUtW4k7nZnZsO58dwFrrCLLyJCUrTxfHOAOQBTpkyJ/v7+TY65ZN71fO6++n+Cy07e9PiiGRgYoF65yqDMeTczs3yNuduGpEmSbpP0oKQHJH08pe8saaGkh9O/O6V0SbpY0lJJ90o6sF2FMBujJ1J3DNK/q1L6SmBSzXG7p7RG6Wa5cn1sVSBpmaT7JN0j6e6U5hi2wmmlz/N6YGZE7AscDJwuaV9GOQjLLEcLgGnp+TTg+pr0U1LlfDCwNnXvuBk4XNJOqQI/PKWZ5c31sVXFuyJi/4iYkrYdw1Y4Y248R8RjQ9N3RcSzwBKy/p+jHYRl1nGSrgJ+CrxO0gpJ04HZwGGSHgbenbYBbgIeAZYCXwP+BiAiVgOfAe5Kj0+nNLNcuT62CnMMW+G0pc+zpMnAAcAiRj8I67GatKYGWkHjwVajHQTUaMBW3gYGBio/qKmb5YuIExvsOrTOsQGc3uA8lwOXtzFrZm3V7fq4XXUxFLM+7oW6GAoziDaAH6TxJ19NY0c6FsMFKfMGRcpPVfLSqUH5LTeeJW0PfAf424h4JltfIjOWQVjNDLSCxoOtRjvQ6tQxDObrhmUn91d+UFPVy2fWbXnUx+2qi6GY9XEv1MVQmPr47RGxUtIfAAsl/bJ2Z7tjuCBl3qBI+alKXhrVKa0Oym9pnmdJW5JV1PMi4rspebSDsMzMrEWuj63sImJl+ncVcB3ZYlSOYSucVmbbEHAZsCQiPl+za7SDsMzMrAWuj63sJG0naYeh52QDsu/HMWwF1Eq3jbcBHwbuk3RPSvsk2aCra9KArEeB49K+m4CjyQZhPQec1sJ7m5nZy1wfW9n1AdelrkbjgH+LiO9LugvHsBXMmBvPEXE7oAa7RzUIy8zMxs71sZVdRDwCvKlO+lM4hm0EY1mIrlU9s8JgHh+umZltzHWxmZVdSwMGzczMzMx6iRvPZmZmZmZNcuPZzMzMzKxJbjybmZmZmTXJjWczMzMzsya58WxmZmZm1qSemaqubCbPupGZ+63fZF32ZbOPySlHZma9x3WxmQ3nxrOZmbWd53M2s6qqXOPZFbaZmZmZdYr7PJuZmZmZNcmNZzMzMzOzJlWu20bVNeqW4sErZmbd47rYrLuK1C3XV57NzMzMzJrkK89mZmZmVgjDrzDXmyoyb248m5mZmVlXFakbxmi524aZmZmZWZN85dnMzMzMOqLMV5gbcePZzMzMzMasig3kkXS98SzpSOAiYAvg6xExu9t5MGuFY9iqwHFsVeA47oz7Vq4t3CC9Iulqn2dJWwBfAo4C9gVOlLRvN/Ng1grHsFWB49iqwHFseen2lee3AEsj4hEASfOBqcCDXc5H5Yx2wv6x3GLpxuT/JVh4oCsxXILPwcrNdXGHjOVvd7T1seviDUoTxyN9x+38f7pdZu6X21uXQrcbzxOB5TXbK4CDag+QNAOYkTYHJT3U4FwTgCfbnsMC+VgbyqgL2pSZNp8rabp8I7z3nu3KTJM2G8PQdByP+vvtwHfQijL/DRYt7z0Vx2XTA3UxNFnGAtXF0HqbohCxW/OZFiI/0J6Yb5dO5KXVOC7cgMGImAPM2dxxku6OiCldyFJuql7GKpevmTgue/nLnP8y572beiGOm+EyltdIMVy0MhcpP87LyLo9z/NKYFLN9u4pzawsHMNWBY5jqwLHseWi243nu4B9JO0laSvgBGBBl/Ng1grHsFWB49iqwHFsuehqt42IWC/pDOBmsmllLo+IB8Z4us127aiAqpexdOVzDG+kzPkvc95b5jgeNZexgNoQx0Urc5Hy47yMQBGRdx7MzMzMzEqh2902zMzMzMxKy41nMzMzM7Mmla7xLOlISQ9JWippVt75aQdJl0taJen+mrSdJS2U9HD6d6c889gqSZMk3SbpQUkPSPp4Sq9UOZtVhjiWtEzSfZLukXR3Sqv7fSlzcSrPvZIOzCG/Tf8djZRfSdPS8Q9LmtbtcpRFGWJ4LKpeH/dKXby5+JT0Dkk/k7Re0rHD9rW1DmgxLy+lOvgeSW0ZDNlEfs5K8XGvpFsk7Vmzr9ufzUh5aftn07SIKM2DbEDAr4HXAlsBvwD2zTtfbSjXO4ADgftr0v4vMCs9nwVckHc+WyzjbsCB6fkOwK/IllOtVDmb/CxKEcfAMmDCsLS63xdwNPA9QMDBwKIc8tv031Gj/AI7A4+kf3dKz3fK+7so2qMsMdzpOCrjoxfq4mbiE5gM/DFwJXBsTXpb64BW8pL2Debw2bwLeGV6/tfA1Tl+NnXz0onPZjSPsl153rAUZ0S8CAwtxVlqEfEjYPWw5KnA3PR8LvD+rmaqzSLisYj4WXr+LLCEbHWoSpWzSWWO40bf11TgysjcAYyXtFs3MzbKv6NG+T0CWBgRqyPiaWAhcGTnc186ZY7hEVW9Pu6Runiz8RkRyyLiXuD3w17b7jqglbx0QjP5uS0inkubd5DNnw35fDaN8pKrsjWe6y3FOTGnvHRaX0Q8lp4/DvTlmZl2kjQZOABYRIXLOYKyxHEAP5C0WNkSt9D4+ypqmUab36KWo2h67XOqZD1V4bq4lfhsd2y3er5tJN0t6Q5J7fhBM9r8TCe7SzeW13YyL9D+z6ZphVue2zYVESGpEnMKStoe+A7wtxHxjKQN+6pUzop4e0SslPQHwEJJv6zdWbbvq2z5tWKqShy5Li6NPVM9/FrgVkn3RcSvu/HGkv4cmAK8sxvvN4a85PbZlO3Kcy8txfnE0G3v9O+qnPPTMklbklXW8yLiuym5cuVsQiniOCJWpn9XAdeR3WJr9H0VtUyjzW9Ry1E0vfY5Vaqe6oG6uJX4bHdst3S+mnr4EWCA7E5BK5rKj6R3A/8AvC8iXhjNa7uUl058Nk0rW+O5l5biXAAMjWSdBlyfY15apuyyxmXAkoj4fM2uSpWzSYWPY0nbSdph6DlwOHA/jb+vBcApyhwMrK25BZyn0eb3ZuBwSTul2QYOT2m2scLHcJtVpp7qkbq4lfhsdx0w5rykPGydnk8A3gY82EJemsqPpAOAr5I1Vmt/RHX9s2mUlw59Ns3La6TiWB9ko+R/RTZC8x/yzk+bynQV8BjwO7I+P9OBXYBbgIeBHwI7553PFsv4drI+tPcC96TH0VUr5yg+j0LHMdno51+kxwNDeWz0fZHNWvGlVJ77gCk55Lnpv6OR8gt8BFiaHqfl/V0U9VH0GO5GHJXx0St1cb34BD5N1ggD+JP0/a4DngIeqHltW+uAseYF+NNUP/0i/Tu9S5/ND4EnauJjQY6fTd28dOqzafbh5bnNzMzMzJpUtm4bNgJJp0q6Pe98mI2WpG0l/buktZK+nXd+zFrheLaykHSepG+l53tIGpS0Rd75KjrPtmFmRXAs2dRYu0TE+rwzY9Yix7OVTkT8N7B93vkoA195NrMi2BP4VaOGhiT/0LcyGTGezazc3HgeI0nLJP19Wm99naTLJPVJ+p6kZyX9MI1GRdLBkv5T0hpJv5DUX3Oe0yQtSa95RNJf1ezrl7RC0kxJqyQ9Jum0mv27SFog6RlJdwJ/OCyPfyrprnTr8C5Jf1qzb0DSZ1O+BtMtxl0kzUvnu0vZBPpWcXnHsqRPAf8MHJ9icXrqgvQTSRdKego4Lx37kfQeT0u6WdKeNe9xmKRfpnj/oqT/kPQXXfkQrTAKGs8bbo2nYyZLiqEfhak+/kyK+Wcl/UDZDAJmQFvjeq9UNz4raSEwoWbf8Lgc099AT8h7RGxZH8AysqUi+8hWxFkF/IxsnsFtgFuBc9O+p8hGlL4COCxt75rOcwxZo1dkk38/BxyY9vUD68lGnm6ZzvEcaS15sqUsrwG2A/4X2fyIt6d9OwNPAx8m655zYtreJe0fIBst+4fAjmRTvPwKeHc6/krgG3l/zn70TCyfB3yrJk+npuPPTPG4LdmyrUuBN6S0fwT+Mx0/AXiW7Hb5lsD/Sa//i7w/Xz8cz3W2J5PNeDEubQ+QzTbwRynWB4DZeX+WfhTn0ca4/inweWBr4B2p3vxW2jc8Lsf8N1D1h688t+aSiHgisom6fwwsioifR8TzZItKHAD8OXBTRNwUEb+PiIXA3WSBRkTcGBG/jsx/AD8A/qzmPX4HfDoifhcRNwGDwOuUdej/IPDPEbEuIu4H5ta87hjg4Yj4ZkSsj4irgF8C76055hvpvdeSLXn564j4YWS3Gr9NFycct9zlFssj5Ok3EXFJit//AT4K/GtELEkx+i/A/unq89Fk0ztdGxG/A75AtsSw9aYixvPmfCMifpVi/Rpg/xbOZdXUUlxL2oNsWrx/iogXIuJHwL83erMc/gZKw43n1jxR8/x/6mxvT9b37UPp9skaSWvI5tkcWsnpKGXrsq9O+46m5jYK8FRs3G/uuXTeXcmuvtWuC/9ozfPXDNse2l+7bnwz+bfekGcsN7J82PaewEU1772a7IrIRLJ433B8RESd11vvKGI8b07tj71Wz2XV1GpcvwZ4OiLW1bxueDthgxz+BkrDg3A6bznwzYj4y+E7lK2O8x3gFOD6iPidpP9H1iDYnN+S3TKZRHZFGWCPmv2/IfsjqrUH8P3RZd9sg07FciPDJ6FfDpwfEfPqvP8+1CzzKklsvOyr2XDdjOd1wCtrtl89xvOYbc5Icb0nsJOk7Woa0HuwaV3bqTq9MnzlufO+BbxX0hGStpC0TepovzuwFVm/o98C6yUdRbbc5WZFxEvAd4HzJL1S0r68vLQqwE3AH0k6SdI4SccD+wI3tLFs1ls6Esuj8BXgHElvBJC0o6QPpX03Am+U9IE02OVjuIFiI+tmPN8DvEPZPLo7Aue0nHuz+hrGdUQ8StaF41OStpL0djbuylmrG3V6abnx3GERsZxsoNMnyYJwOfD3wCsi4lmy/+SvIRvMdxJNrnmfnEF2i+Rx4ArgGzXv+xTwHmAm2WCBTwDviYgnWyuR9aoOx3Iz738dcAEwX9IzwP3AUWnfk8CHgNlk8b4P8JN2vr9VSzfjOfU7vZpsSezF+CKGdchIcZ0OOQk4iKzb27lkkwPUO0/H6/Qy8/LcZlZJkgbIRpF/Pe+8mJlZdfjKs5mZmZlZk9x4NjMzMzNrkrttmJmZmZk1yVeeredJGi/pWmVLOy+R9FZJO0taKOnh9O/QsqeSdLGkpcqWST0w7/ybmZlZ9xT6yvOECRNi8uTJm6SvW7eO7bbbrvsZGqWy5BPKk9fh+Vy8ePGTEbFrK+eUNBf4cUR8XdJWZPOxfhJYHRGzJc0iW3L0bElHky0ZfTTZiOWLIuKgkc7fKI6Lpiwx0A5FK2s74rjTihbHRfsOiyDPz6QsMbzrrrtWIm6qEP9FLEPTcRwFWCO80ePNb35z1HPbbbfVTS+asuQzojx5HZ5P4O5oIcaAHYH/Iv2QrEl/CNgtPd8NeCg9/ypwYr3jGj0axXHRlCUG2qFoZW01jrvxKFocF+07LII8P5OyxHBV4qYK5ShiGZqNY68waL1uL7K5ML8h6U1kc7B+HOiLiMfSMY8Dfen5RDZe9nlFSnusJg1JM4AZAH19fQwMDHQq/20zODhYiny2Qy+V1czM2suNZ+t144ADgTMjYpGki4BZtQdEREgaVf+miJgDzAGYMmVK9Pf3tym7nTMwMEAZ8tkOvVRWMzNrLw8YtF63AlgREYvS9rVkjeknJO0GkP5dlfavBCbVvH73lGZmZmY9oOevPE+edWPDfctmH9PFnFgeIuJxScslvS4iHgIOBR5Mj2lkyz1PA65PL1kAnCFpPtmAwbU13TusIBr9Xftvure5vjfrvkZ/d1ccWazBgqPR841nM7LZM+almTYeAU4juytzjaTpwKPAcenYm8hm2lgKPJeONTMzsx5RucazrzjZaEXEPcCUOrsOrXNsAKd3PFNmZmYlMdJdnSpyn2czMzMzsyZV7sqzmZlZ0bi/tVl1+MqzmZmZmVmT3Hg2MzMzM2uSu22YWWn12iAVa47jwsw6yVeezcxKTtI2ku6U9AtJD0j6VErfS9IiSUslXZ2mY0TS1ml7ado/Oc/8m5mVia88m5mV3wvAIRExKGlL4HZJ3wPOAi6MiPmSvgJMBy5N/z4dEXtLOgG4ADg+r8wXgac5NbNmufFsZlZyaf7xwbS5ZXoEcAhwUkqfC5xH1niemp5DtiT9FyUpncfMrOPuW7mWU+v8aC3DD1Y3nsfAVyjMrGgkbQEsBvYGvgT8GlgTEevTISuAien5RGA5QESsl7QW2AV4squZNjMrITeeR+BBJ2ZWFhHxErC/pPHAdcDrWz2npBnADIC+vj4GBgZaPWXbDA4ONszPzP3W100fi3aVeaQ8tes9RvpMzKx9xtx4lrQN8CNg63SeayPiXEl7AfPJrmIsBj4cES9K2hq4Engz8BRwfEQsazH/ZmZWIyLWSLoNeCswXtK4dPV5d2BlOmwlMAlYIWkcsCNZvTz8XHOAOQBTpkyJ/v7+LpSgOQMDAzTKT71bwWO17OT679FI44sujf+7He17NDLSZ2Jm7dPKbBtDA1TeBOwPHCnpYLKBJxdGxN7A02QDU6BmgApwYTrOzMxaJGnXdMUZSdsChwFLgNuAY9Nh04Dr0/MFaZu0/1b3dzYza86YG8+RaTRA5dqUPhd4f3o+NW2T9h8qSWN9fzMz22A34DZJ9wJ3AQsj4gbgbOAsSUvJ7gZelo6/DNglpZ8FzMohz2ZmpdRSn+dODFBppo/dWPq65dE3rkz9z8qS17Lk06ybIuJe4IA66Y8Ab6mT/jzwoS5kzcysclpqPHdigEozfezG0tetUZ+yTvaNK1P/s7LktSz5NDMzs2pqywqDEbGGrG/dhgEqaVe9ASqMNEDFzMzMzKyoxtx49gAVMzMzaydJW+j/Z+/u4+Sq69COBEYAACAASURBVPvvv94CIhJquInbGKhLa5pe1KjQFOiF1VW8QbTGthRRRIL8LtpfwcLPtSVgf8WqXFfsQ9B487OiUIJFAqKWVPEmIiOlBRQQiUCRiKEkBiIQIgFFl36uP853wuxmZvfMzs05Z+b9fDzmsTPfc/bM98x+5uz3fG+l70n6cnrtJeatdDrptjEfWJX6PT8DuCIivizpTmC1pA8A32PyAJXPpgEqjwDHdfDebfOczWZmVkZeeGuS08kq4n4tva7P4OUl5q00Zl149gAVMzMz6xZJ+wOvB84lmyVGeIl5KyGvMNhFU2sPxhdPsGz5V4a19sDMzKwdHwH+Btgrvd6XLs/gNSgzNpXtPGYzc9nIHs1/r0zn1YoLz2ZmZlYoSW8AtkTELZLGunXcqTN4zZkzZyBmbCrbzFOzmblsfPEE563buRjarRU3e8mFZzMzMyvaEcAbJR0NPIusz/NKOlxi3qwXXHg2MzOzQkXEWcBZAKnm+d0Rcbykz5PN0LWa5jN43YBn8BooVRhA68KzmZlVkmdRGgpnUsIZvGy4ufBsZmZmpRERNaCWnnsGLyudrqwwaFZ1npjfzMzM8nDNcx9Uof+OeWJ+MzMzm5lrnm3oNUzM/5n0uj4x/5Vpl1XAm9Lzpek1afuRaX8zMzMbAq55NuvDxPxVmPS9bJPu59HuxPz186viuZqZWTm48GxDrV8T85dpMvtWyjbpfh7tTsxfn3y/iudqZmbl4MKzDTtPzG9mZma5uc+zDbWIOCsi9o+IUbJ5Qr8VEccD15JNvA/NJ+YHT8xvZmY2dFx4NmvuTOBdaQL+fZk8Mf++Kf1dwPKC8mdmZmYFcLeNAk23Opanses/T8xvZmZmM6ls4dnLspqZWVH8P8hseLnbhplZxUk6QNK1ku6UdIek01P6PpLWSron/dw7pUvSR9NKmbdLOqTYMzAzqw4Xns3Mqm8CGI+Ig4DDgVMlHUTWJ/+aiFgIXMPTffRfByxMj1PIVs80M7McXHg2M6u4iNgcEbem54+RLTW/gMkrYk5dKfOSyNxINjXj/D5n28yskmbd51nSAcAlwAgQwAURsVLSPsDlwCiwATg2IramJYxXAkcDTwDL6hd7M7N+qPdTHV88sdMCK4MySFfSKHAwcBMwEhGb06YHyK7X0LBSZlJfRXNzQ1qpV8rcvn0744uf6vn7tDrndle37OZ7t+KVM63X3Nc/08mAwXoz4a2S9gJukbQWWEbWTLhC0nKyZsIzmdxMeBhZM+FhnWTezMyeJmkO8AXgjIj4WVZnkYmIkNTWnORlXimzVqtx3vWP9/x96qtSTtXu6pbdfO9WvHKmWX/MuvCcajM2p+ePSWpsJhxLu60im/rrTBqaCYEbJc2VNL+hVsTMrCnXdsxM0m5kBedLI+KLKfnB+nU2dcvYktLrK2XWNa6iaWZm0+jKVHX9bibsV3Ndp0b2mH3TXr+b3qrS3FeVfJr1U+oWdyFwV0Sc37CpviLmCnZeKfM0SavJWgC3uSLDzCyfjgvPRTQT9qu5rlPjiyc4b93sPuJ2m+s6VZXmvqrk06zPjgBOANZJui2lnU1WaL5C0snAfcCxadvVZONP1pONQTmpv9k1M6uujgrPbiY0MyteRFwPqMXmI5vsH8CpPc2UdaxVd6VBGdxqVlWdzLbhZkIzMxto7m9vZlN1UvPsZkIzM+u5ZgXYbDxJV4btmJm1pZPZNtxMaGZd5Vo+MzMrO68waGZmZmaWk9u8SsoDRczMzMzKxzXPZmZmZmY5ufBsZmZmZpaTu22YmZmZWamVqTura57NzMzMzHJy4dnMzMzMLCcXns3MzMzMcnLh2czMzAol6QBJ10q6U9Idkk5P6ftIWivpnvRz75QuSR+VtF7S7ZIOKfYMbJi48GxmZmZFmwDGI+Ig4HDgVEkHAcuBayJiIXBNeg3wOmBhepwCfLL/WbZh5cKzmZmZFSoiNkfEren5Y8BdwAJgKbAq7bYKeFN6vhS4JDI3AnMlze9ztm1Ieao6MzMzKw1Jo8DBwE3ASERsTpseAEbS8wXA/Q2/tjGlbW5IQ9IpZDXTjIyMsH37dmq1Wq+y3jdFncf44omuHWtkj+4cr4jPwYVnMzMzKwVJc4AvAGdExM8k7dgWESEp2jleRFwAXACwZMmSmDNnDmNjY13McTFqtVoh57GsxVzLszG+eILz1nVeDN1w/FjnmWmTu23YUPMgFTOzcpC0G1nB+dKI+GJKfrDeHSP93JLSNwEHNPz6/inNrOdc82zDrj5I5VZJewG3SFoLLCMbpLJC0nKyQSpnMnmQymFkg1QOKyTn1lVlWr3KbNgoq2K+ELgrIs5v2LQGOBFYkX5e1ZB+mqTVZNfgbQ3dO8x6yoVnG2rpYrs5PX9MUuMglbG02yqgRlZ43jFIBbhR0lxJ833RNjPryBHACcA6SbeltLPJCs1XSDoZuA84Nm27GjgaWA88AZzU3+zaMHPhuWJcO9Y7vRykUoUBKv0agLJu07aW28YX9/ztgfYGqlThb2dWdRFxPaAWm49ssn8Ap/Y0U2YtuPBsRu8HqVRhgEq/BqB0c8DJbLUzUKWIwSjtknQR8AZgS0S8MKXtA1wOjAIbgGMjYmtqHl9JVmv3BLCsPkWYmZnNzAMGbeh5kIoNgIuBo6akeXEJM7Me6KjwLOkiSVsk/aAhzbMUWGXkGKQCOw9SeXuK58PxIBUrgYi4DnhkSrIXlzAz64FOu21cDHwcuKQhrV7b4VkKrAo8SMUGVUf99q28Wo19ufioPfucE7Ph1FHhOSKuS4OsGnmWAqsMD1KxYTCbfvtQnoGvzQZ3dmt1skEyKKvnmZVdLwYM9nyWgu3btzO++Knu5roH+nlx7/SCWZWLblXyaVYCD9YrKGbbb78sA1+bDTLt1upkg+Tio/YciNXzzMqup1eeXs1SUKvVOO/6x7uSx17q58W90xkBilrqs11VyadZCXhxCTOblqe/nZ1elOw6ru0wM7P8JF1G1l1uP0kbgXNwv30zS1oVkru1/7DpReHZtR1mZn0UEW9pscn99s3MuqyjwrNrO8zMzMysKNPVkveq+0mns224tsPMmhqUZj/3CTQzs0Yeqjwg/A/erL/8nTMzG04uPJuZWSkMSmuFmQ22jpbnNjMzMzMbJi48m5mZmZnl5G4bA879Ms3MzMy6xzXPZmZmZmY5ufBsZmZmZpaTu22YWUc8Q4KZmQ0TF57NzMwGwLpN21jmcS5DyxUZ/ePC85Ca+iUbXzyx46Lri6yZmZlZc+7zbGZmZmaWk2uezcysb9y0bGZV58KzmeXiQo+ZWfFGl39lUldL6z932zAzMzMzy8k1z7YTr0poZmZm1pxrns3MzMzMcnLNs5lN4r7NZmZmrbnwbGZmNuDcHc+se1x4NjOzrnMLhlln/B3qXK9uGvteeJZ0FLAS2AX4TESs6HcebHba/SIPao3GIMRws7+lpz7qjum+J2X6TgxCHJtVJY6rcl2wfPpaeJa0C/AJ4NXARuC7ktZExJ39zIfZbFUthl1zYc10M44dY1aUMl6PZ/N98Heoevpd83wosD4i7gWQtBpYCpSy4GGdGdA+dqWMYV98rU2ljGPrv4q3KPY8jn1ttWb6XXheANzf8HojcFjjDpJOAU5JL7dLurvJcfYDHupJDrvoryqST+hvXvXBjn59aj6f39HR2jdjDEPuOC6VKsVrp4o612li33HcpmGK17z68ZmUKIZhFmWKV7ziFQ8zAHEzCPFf5Dl0GselGzAYERcAF0y3j6SbI2JJn7I0a1XJJ1Qnr1XJZ544LpuqfLbdMEzn2okyx7H/hjvzZ7KzqTE8KJ/RIJxHlc+h34ukbAIOaHi9f0ozqwrHsA0Cx7ENAsexFaLfhefvAgslHSjpmcBxwJo+58GsE45hGwSOYxsEjmMrRF+7bUTEhKTTgK+TTStzUUTcMYtDlbIZsYmq5BOqk9dC89nFGC6jqsRANwzTue5kQOJ4qP+GLQzVZzLLOB6Uz2gQzqOy56CIKDoPZmZmZmaV0O9uG2ZmZmZmleXCs5mZmZlZTpUrPEs6StLdktZLWl50fpqRdJGkLZJ+UHRepiPpAEnXSrpT0h2STi86T81Iepak70j6fsrn3xedpyqb6Tsk6WWSbpU0IemYIvLYDTnO810p9m+XdI2kIuaptRkMS7zm5bjOJ8fntLuky9P2mySN9j+X0xuU2B/ImI2IyjzIBgT8CPhN4JnA94GDis5Xk3y+DDgE+EHReZkhn/OBQ9LzvYAflvTzFDAnPd8NuAk4vOh8VfGR5zsEjAIvAi4Bjik6zz08z1cAz07P/ydwedH59mNWf8fKx2uXP4+hj+ucn9NfAv+Ynh9Xts9pUGJ/UGO2ajXPO5bijIhfAvWlOEslIq4DHik6HzOJiM0RcWt6/hhwF9mKTaUSme3p5W7p4ZGuszPjdygiNkTE7cB/F5HBLslzntdGxBPp5Y1kc8RauQxLvObluM4nT1lhKbAqPb8SOFKS+pjHmQxK7A9kzFat8NxsKc7SFfaqKDVZHUxWq1s6knaRdBuwBVgbEaXMZwUMy3eo3fM8GfhqT3NkszEs8ZqX4zqfPJ/Tjn0iYgLYBuzbl9zlMyixP5AxW7rlua3/JM0BvgCcERE/Kzo/zUTEU8BLJM0FviTphRFR6j7lVg2S3gYsAV5edF7MusVxbVVTpZitWs2zl+LsMkm7kRWcL42ILxadn5lExKPAtcBRReelooblO5TrPCW9CngP8MaIeLJPebP8hiVe83Jc55Pnc9qxj6RdgecAD/cld/kMSuwPZMxWrfDspTi7KPXvuhC4KyLOLzo/rUial2qckbQH8GrgP4vNVWUNy3doxvOUdDDwKbKL9ZYC8mgzG5Z4zctxnU+euFkDnJieHwN8K9KItZIYlNgfzJgtesRiuw/gaLJZIX4EvKfo/LTI42XAZuBXZP17Ti46Ty3y+VKygXe3A7elx9FF56tJPl8EfC/l8wfA3xWdpyo/mn2HgPeRXbgAfj/F7eNkNTF3FJ3nHp3nN4EHG2J/TdF59mNWf8eBiNcufh6O63yf07OAzwPrge8Av1l0nmdxDpWI/UGMWS/PbWZmZmaWU9W6bQwMSb8habukXWb5+xtSHyGzUmg3piX9saT70+8c3Ov8mfWDpIslfaDofJi10hijkv5Q0t0dHCskvaB7uasGz7ZRkIj4L2BO0fkw65ZZxPSHgNMi4qoeZcnMzKYREf8GLCo6H1XjmmczK8rzgTuKzoSZmVk7XHjustSd4q/TGu2PS7pQ0oikr0p6TNI3Je0taTQ1d+yafq8m6f2S/j3t9w1J+zUc9wRJ90l6WNJ7przn7pI+Iukn6fERSbunbWOSNkr6G0lbJG2W9CZJR0v6oaRHJJ3d30/JqqTbMZ3idTvZsq3fl/SjtP+k5r8pTYv1OB5viOOTivg8bDDkjeu07xsl3SHp0RTX/1fDcQ6WdGv6ncvJBqI1vs8bJN2Wfvc/JL2oz6dqFdWPGK1fW6e857vTe26TdLmkxv3/Ol1/fyLpHQ3pz0xx/s70epd07f+7Hn9MhXDhuTf+lGw6td8G/ohstZyzgXlkn/lftfi9twInAc8lWwP+3QCSDgI+CZwAPI9sFaTG5SvfAxwOvAR4MdlymH/bsP3Xyb4sC4C/Az4NvA34PeAPgf8t6cAOztcGX9diOiKejIh6944XR8Rv5czDr5PNxbqAbBWqT9T/cZjN0oxxLem3yWZQOiOlXw38ayosPBP4F+CzwD5kszf8af3gyvryXwT8Odl1+1PAmnrlhlkOPY3RFo4lW0vhQLLZrpYBSDqKrFzyamAhsGPcVWRLb78NeF8quC8nqyA5t6OzLykXnnvjYxHxYERsAv4NuCkivhcRvwC+RLYMdjP/FBE/jIifA1eQFYYhm4PyyxFxXWSTh/9vJq9lfzzwvojYEhE/Bf6erKBd9yvg3Ij4Fdm68vsBKyPisYi4A7iTrNBt1kq3Y3o2fkUW57+KiKuB7bivnnUmT1y/GfhKRKxN19APAXsA/zdZpcVuwEdSXF5JNq9t3SnApyLipoh4KiJWAU+m3zPLo9cx2sxHI+InEfEI8K88fd0+luya/oOIeBx4b+MvRbbq7wfICuvvBk6IbHXggePCc2882PD8501etxpU9UDD8yca9nseDWvDp6BtXAnpecB9Da/vS2l1DzcE8M9b5NGDF2063Y7p2Xg4Iia6eDyzPHE96foaEf9Ndj1ekLZtislzvjZei58PjKem9EclPUq22lrj9dlsOr2O0WZylUVaHGcVWdxfHRH3zPA+leXCczVspmF5S0nPJmsCrPsJWbDW/UZKM6uSJ4BnN7z+9aIyYtZg0vVVksiux5vIrs0LUlrdbzQ8v5+s1W9uw+PZEXFZPzJuQ6OTGG3HpLJIi+P8H+DLwGslvXSW71N6LjxXw5XAGyS9NPVfeh+T/3aXAX+rbBnr/cj6Nf9zAfk068RtwFvTQJOjgJcXnSEzsu5Gr5d0pKTdgHGyrhf/AdwATJD1O91N0p+QjTmp+zTwF5IOU2ZPSa+XtFe/T8IGWicx2u77LJN0UKrEO6dxo6QTyMZSLSMbB7NK0kC2DrrwXAGpX/KpwOfI7vy2ki3JWfcB4Gay5avXAbemNLMqOZ1sQMyjZP34/6XY7JhBRNxNNhDqY8BDZDH6RxHxyzRI6k/ICguPkPU9/WLD794M/D/Ax8mu2+vTvmZd00mMtvk+XwU+AnyLLJa/Vd8m6TfStrdHxPaI+BxZueTDszytUvPy3GZmZmZmObnm2czMzMwsJxeezczMzMxycuHZzMzMzCwnF57NzMzMzHLategMTGe//faL0dHRSWmPP/44e+65ZzEZ6jGfW/tuueWWhyJiXtcP3EVz586NF7zgBUVnozCDHNd55Dn/KsRxs+sxVO/vW7X8QjXyXOUYhmp8xv0w7J9D3jgudeF5dHSUm2++eVJarVZjbGysmAz1mM+tfZJmWimpcCMjIzvF8TAZ5LjOI8/5VyGOm12PoXp/36rlF6qR5yrHMFTjM+6HYf8c8saxu22YmZmZmeXkwrOZmZmZWU4uPJuZmZmZ5VTqPs/TGV3+labpG1a8vs85MSuevw9mg6f+vR5fPMGyhu+4v9fF8zV3uLnm2czMzMwsJxeezczMzMxyqmy3DTMzs0HQqguAmZXTjDXPkg6QdK2kOyXdIen0lL6PpLWS7kk/907pkvRRSesl3S7pkIZjnZj2v0fSib07LTMzMzOz7svTbWMCGI+Ig4DDgVMlHQQsB66JiIXANek1wOuAhelxCvBJyArbwDnAYcChwDn1AreZmZmZWRXMWHiOiM0RcWt6/hhwF7AAWAqsSrutAt6Uni8FLonMjcBcSfOB1wJrI+KRiNgKrAWO6urZmJmZmZn1UFsDBiWNAgcDNwEjEbE5bXoAGEnPFwD3N/zaxpTWKt3MzMzMrBJyDxiUNAf4AnBGRPxM0o5tERGSohsZknQKWXcPRkZGqNVqk7Zv376dWq3G+OKJpr8/df8qqZ/bICr63CRdBLwB2BIRL0xp+wCXA6PABuDYiNiqLLhXAkcDTwDL6q0vqa/+36bDfiAiVmFWMEnPAq4Ddie7rl8ZEedIOhBYDewL3AKcEBG/lLQ7cAnwe8DDwJsjYkMhmTczq5hchWdJu5EVnC+NiC+m5AclzY+IzalbxpaUvgk4oOHX909pm4CxKem1qe8VERcAFwAsWbIkxsbGJm2v1WqMjY1NmjC+0Ybjx5qmV0H93AZRCc7tYuDjZAWGunq//RWSlqfXZzK53/5hZP32D2vot78ECOAWSWtSNySzIj0JvDIitqfr9fWSvgq8C/hwRKyW9I/AyWTxfDKwNSJeIOk44IPAm4vKvJlZleSZbUPAhcBdEXF+w6Y1QH3GjBOBqxrS355m3Tgc2Ja6d3wdeI2kvdNAwdekNLOei4jrgEemJLvfvg2EFKvb08vd0iOAVwJXpvSpMV6P/SuBI9XYnNiGdZu2Mbr8Kzs9zMwGVZ6a5yOAE4B1km5LaWcDK4ArJJ0M3Accm7ZdTdbcvZ6syfskgIh4RNL7ge+m/d4XEVMLM2b91LN++43dj+bNm9fzLitl7sZUdJedovXr/CXtQtY14wXAJ4AfAY9GRD04GuN1RyxHxISkbWRdOx6acsxpu9EBjOzRPP7K+jcvYzy2+v7WTf2My5b/bpH0v4D/QXbjt46s/DAfdz2ykpmx8BwR1wOtaiSObLJ/AKe2ONZFwEXtZNCsH7rZbz8db0f3o0WLFu3U/ajbytyNqQRddgrVr/OPiKeAl0iaC3wJ+J0uHHPabnQAH7v0Ks5bt/O/kjLEXjNljMdW39+68cUTkz7jsn62nZC0APgr4KCI+LmkK4DjyCrj3PXISsXLc9swezB1x6CNfvvN0s1KIyIeBa4F/oCsy1G91NUYrztiOW1/DlntnVmRdgX2SDH5bGAzfeh6ZNYuF55tmLnfvg0ESfNSjTOS9gBeTTYn/7XAMWm3qTFej/1jgG+lVkOzQkTEJuBDwH+RFZq3kXXTyNX1KO2/bz/zbMMr91R1ZlUm6TKy2V72k7SRbNYM99u3QTEfWJX6PT8DuCIivizpTmC1pA8A3yMb/E36+VlJ68kG0h5XRKbN6lKFxFLgQOBR4PN0YUB2nn770H5f+DKPM+lEGccElJELzzYUIuItLTa5375VXkTcTraA1dT0e4FDm6T/AvizPmTNLK9XAT+OiJ8CSPoi2YQFcyXtmmqXm3U92jhd16M8/fah/b7wZR5n0okyjgkoIxeezQoy3XReG1a8vo85MTMr3H8Bh0t6NvBzsoqNm3m669Fqmnc9ugF3PbI+c59nMzMzK1RE3EQ28O9WsmnqnkFWY3wm8K7UxWhfJnc92jelv4tskSuzvnDNs5mZWY91c+GYVseqeotVRJxDNh6lkbseWem45tnMzMzMLCcXns3MzMzMcnLh2czMzMwsJxeezczMzMxycuHZzMzMzCwnF57NzMzMzHJy4dnMzMzMLCfP82xmZjYAvGqpWX/MWPMs6SJJWyT9oCHtvZI2SbotPY5u2HaWpPWS7pb02ob0o1LaekleCcjMzMzMKidPt42LgaOapH84Il6SHlcDSDoIOA743fQ7/0fSLpJ2AT4BvA44CHhL2tfMzMzMrDJmLDxHxHXAIzmPtxRYHRFPRsSPgfVky2oeCqyPiHsj4pfA6rSvWaEkLWpoQblN0s8knTGb1hUzMzMbfJ30eT5N0tuBm4HxiNgKLABubNhnY0oDuH9K+mHNDirpFOAUgJGREWq12qTt27dvp1arMb54ommmpu5fJfVzG0RlPbeIuBt4CUBqIdkEfAk4iax15UON+09pXXke8E1Jvx0RT/U142ZmZlaI2RaePwm8H4j08zzgHd3IUERcAFwAsGTJkhgbG5u0vVarMTY2xrIWAyM2HD/WNL0K6uc2iCpybkcCP4qI+yS12mdH6wrwY0n11pUb+pRHMzMzK9CsCs8R8WD9uaRPA19OLzcBBzTsun9KY5p0s7I4Dris4XW7rSs7NLagzJs3r2mte6vWE2i/BaXMLTFlbXXol2E/f7O8JM0FPgO8kKxy7h3A3cDlwCiwATg2IrYqq+FYCRwNPAEsi4hbC8i2DaFZFZ4lzY+IzenlHwP1mTjWAJ+TdD5Zk/ZC4DuAgIWSDiQrNB8HvLWTjJt1k6RnAm8EzkpJHbWuNLagLFq0aKcWFKBl6wm034JS5paYirQ69Mywn79ZG1YCX4uIY9I1+dnA2cA1EbEizdS1HDiTbAKChelxGNk1u2l3ULNum7HwLOkyYAzYT9JG4BxgTNJLyAoWG4A/B4iIOyRdAdwJTACn1vuCSjoN+DqwC3BRRNzR9bMxm73XAbfWW1Vm2bpiZlZKreaALsv8z5KeA7wMWAaQJhf4paSlZGUQgFVAjazwvBS4JCICuFHS3CkVe2Y9M2PhOSLe0iT5wmn2Pxc4t0n61cDVbeXOrH/eQkOXjVm0rpiZ2ewdCPwU+CdJLwZuAU4HRhquxQ8AI+n5AnaeiGABMKnwPNMkBHXtdq9q1VXuY5de1TR98YLn5D52kdzNLB+vMGhDT9KewKtJLSjJP7TbumJmZrO2K3AI8M6IuEnSSrIuGjtEREiKdg460yQEde12r5qu210zZehCl4e7meWTZ5EUs4EWEY9HxL4Rsa0h7YSIWBwRL4qINzY2BUbEuRHxWxGxKCK+WkyuzZ4m6QBJ10q6U9Idkk5P6ftIWivpnvRz75QuSR9N85XfLumQYs/AjI3Axoi4Kb2+kqww/aCk+ZC1CAJb0nZ3obPCuPBsZlZ9E2QzwhwEHA6cmuYkX0422GohcA1P1+Q1DrY6hWywlVlhIuIB4H5Ji1LSkWQtfGuAE1PaiUC9X8Qa4O3pRvBwYJv7O1u/uNuGmVnFpULD5vT8MUl3kfX/9GArq5J3ApemmTbuJVus6hnAFZJOBu4Djk37Xk02Td16sqnqTup/dm1YufBsZjZAJI0CBwM30YfBViN7NB88VdZBR0UNiJpuXveZtPqMu6FMf6eIuA1Y0mTTkU32DeDUXuep1SwlNtxceDYzGxCS5gBfAM6IiJ81rpTZq8FWH7v0Ks5bt/O/krIOkCpqQFS7A8wajS+eaPoZd0NZ/05mZeY+z2ZmA0DSbmQF50sj4osp2YOtzMy6zIVnM7OKS0sVXwjcFRHnN2zyYCszsy5ztw0zs+o7AjgBWCfptpR2NrACD7YyM+sqF57NzCouIq4H1GJzYYOtzMwGkQvPZmZmXeLZGcwGn/s8m5mZmZnl5MKzmZmZmVlOLjybmZmZmeU0Y+FZ0kWStkj6QUPaPpLWSron/dw7pUvSRyWtl3S7pEMafufEtP89kk5s9l5mZmZmZmWWZ8DgxcDHgUsa0pYD10TECknL0+szgdcBC9PjMOCTPBdO4AAAIABJREFUwGGS9gHOIVt2M4BbJK2JiK3dOhGz2ZK0AXgMeAqYiIglKWYvB0aBDcCxEbE1zae7kmyaryeAZRFxaxH5NjPrVKsBjhtWvL7POTGrjhlrniPiOuCRKclLgVXp+SrgTQ3pl0TmRmBuWtXqtcDaiHgkFZjXAkd14wTMuuQVEfGSiFiSXtdvEBcC16TXMPkG8RSyG0QzMzMbErOdqm6kYTWqB4CR9HwBcH/DfhtTWqv0nUg6haxQwsjICLVabdL27du3U6vVGF880TRjU/evkvq5DaIKnttSYCw9XwXUyFpXdtwgAjdKmitpvldnMzPrnKRdgJuBTRHxBkkHAquBfYFbgBMi4peSdidrEf894GHgzRGxoaBs25DpeJ7niAhJ0Y3MpONdAFwAsGTJkhgbG5u0vVarMTY2xrJWTU3HjzVNr4L6uQ2ikp9bAN9IcfypFIPt3iBOKjw33gTOmzev6Y1DqxtAaP8msMw3kxW8ceqqYT9/szadDtwF/Fp6/UHgwxGxWtI/AieTtfidDGyNiBdIOi7t9+YiMmzDZ7aF5wfrtW2pW8aWlL4JOKBhv/1T2iaersWrp9dm+d5m3fbSiNgk6bnAWkn/2bhxNjeIjTeBixYt2ukmEGh5Awjt3wSW+Way5DdOPTfs52+Wl6T9gdcD5wLvSmNMXgm8Ne2yCngvWeF5aXoOcCXwcUlKrYJmPTXbwvMa4ERgRfp5VUP6aZJWkw0Y3JYK2F8H/t/6rBzAa4CzZp9ts+6JiE3p5xZJXwIOpf0bRDOrMA+cK4WPAH8D7JVe7ws8GhH1prXGLp87WgEjYkLStrT/Q40HnKkraF2rFqLpWgjbUZXWJ7eU5TNj4VnSZWS1xvtJ2kg2a8YK4ApJJwP3Acem3a8mm4VgPdlMBCcBRMQjkt4PfDft976ImDoI0azvJO0JPCMiHkvPXwO8jzZvEPufczOzwSHpDcCWiLhF0li3jjtTV9C6Vi1E07UQtqMMrYB5uKUsnxkLzxHxlhabjmyybwCntjjORcBFbeXOrPdGgC9lrYPsCnwuIr4m6bu0cYNoZuU3uvwrjC+e6FqByLrqCOCNko4GnkXW53kl2axdu6ba58aWvnor4EZJuwLPIRs4aNZzHQ8YNKuyiLgXeHGT9Idp8wbRzMxmJyLOInXnTDXP746I4yV9HjiGbMaNqa2AJwI3pO3fcn9n6xcvz21mZmZldSbZ4MH1ZH2aL0zpFwL7pvR38fRc/GY955pnMzMzK42IqJFm5Eqtg4c22ecXwJ/1NWNmiQvPZgPMMwiYmZl1l7ttmJmZmZnl5JpnsxJyjbGZmVk5ufBsZmZmk/gG3qw1F57NzCpO0kVAfZGJF6a0fYDLgVFgA3BsRGxNSx6vJJuv/AlgWUTcWkS+zYZFq5sR8A1JFbnPs5lZ9V0MHDUlbTlwTUQsBK7h6am8XgcsTI9TgE/2KY9mZgPBNc9mZhUXEddJGp2SvBQYS89XkU39dWZKvyQtKHGjpLmS5g/SMvPT1fKZmXXKhWczs8E00lAgfoBsKXqABcD9DfttTGk7FZ4lnUJWO83IyAi1Wm3nN9kDxhdP7JTebN9+aZafulb5baXd82jn2Hm1m+deKvLvalYWLjybmQ24iAhJbS9dHBEXABcALFmyJMbGxnba52OXXsV563b+V7Lh+J337Zdl09Q8jy+eaJrfVto9j+nee7bazXMvFfl3NSsL93k2MxtMD0qaD5B+bknpm4ADGvbbP6WZmVkO5biVNTOzblsDnAisSD+vakg/TdJq4DBg2yD1d+42T9lmZlN1VPMsaYOkdZJuk3RzSttH0lpJ96Sfe6d0SfqopPWSbpd0SDdOwKwTkg6QdK2kOyXdIen0lP5eSZtSbN8m6eiG3zkrxfHdkl5bXO7NMpIuA24AFknaKOlkskLzqyXdA7wqvQa4GrgXWA98GvjLArJsZlZZ3ah5fkVEPNTwuj490gpJy9PrM5k8PdJhZNMjHdaF9zfrxAQwHhG3StoLuEXS2rTtwxHxocadJR0EHAf8LvA84JuSfjsinuprrs0aRMRbWmw6ssm+AZza2xyZtUfSAcAlZANbA7ggIlZ6vnIro170eV5KNi0S6eebGtIvicyNwNx6fzyzokTE5voFNyIeA+4im3mglaXA6oh4MiJ+TFZ7d2jvc2pmNtDqFRkHAYcDp6bKCs9XbqXTac1zAN9Io7g/lUZmdzQ90kxTI23fvp1ardZy2p4qT6NTP7dBVIVzS/PkHgzcBBxB1i/07cDNZBf1rWQxe2PDr9Xj2MzMZimVGzan549JqldkDO185VZenRaeXxoRmyQ9F1gr6T8bN85meqSZpkaq1WqMjY21nA6oytPo1M9tEJX93CTNAb4AnBERP5P0SeD9ZDeI7wfOA97RxvF23ATOmzev6Y3DbOZtbXUD0u6x+nkjU4Ubp14a9vM3a9eUioyO5ysvOw9KrZ6OCs8RsSn93CLpS2TN1w/W7/48PZJVgaTdyArOl0bEFwEi4sGG7Z8Gvpxe5orjxpvARYsWNZ0fdzbzwba6OWz3WP28ySz7jVOvDfv5m7WjSUXGjm2zqZDLs9APtL7JLXJxmiJuun2zn8+sC8+S9gSekZpX9gReA7wPT49kFZIGnVwI3BUR5zekNzb//THwg/R8DfA5SeeTDRhcCHynj1k2G1hVqoHzEuDd16wigw4r5PIs9AOtb3J7sehNXkW0pPtmP59Oap5HgC+lu8Jdgc9FxNckfRe4Ik2VdB9wbNr/arJRsevJRsae1MF7m3XLEcAJwDpJt6W0s4G3SHoJWbeNDcCfA0TEHZKuAO4kG+ByqmfaMDPrTKuKDFwhZyU068JzRNwLvLhJ+sN4eiSriIi4HlCTTVdP8zvnAuf2LFNmNkmVaqRt1lpVZKzAFXJWMl5h0MzMzAo1TUUGuELOSsaFZzMzM7OScYtLebnwbFYhHqRkZmZWrF6sMGhmZmZmNpBceDYzMzMzy8ndNszMrOvcX9PMBpULz2ZmZjbU1m3aVuiCKO3wjWnx3G3DzMzMzCwn1zz3ge8SzczMzAaDa57NzMzMzHJyzbOZmZlZxU23DoBburtraArPs1lcwsFmZmZmZo2GpvA8G+6rbGZmZmaNBq7wXOTyxV462arCzXtmZmaz0/fCs6SjgJXALsBnImJFv/PQqW4VkqceZ3zxxIzzTLpgU7xBiGEzx7ENAsdxPnnLLTOVQ1wGyfS18CxpF+ATwKuBjcB3Ja2JiDv7mY8qc1eSYg1DDLd7c+jYq55BiWO39g23QYnjKnEZJNPvmudDgfURcS+ApNXAUsCBblXhGJ5ipgLM1JqMYbvIllRhcewCr3WRr8cl0c3vdbv/I4rohtjvwvMC4P6G1xuBwxp3kHQKcEp6uV3S3VOOsR/wUM9yWKC/6uDc9MEuZ6b7evV3e34PjjmdGWMYdorjJyX9oA95K6WpcV2BWO22PLFfhThudj2Gil2TO7nOFqVMeZ7m+9vvGIbulCnqSvMZF6kMsdbN/xGzOFauOC7dgMGIuAC4oNV2STdHxJI+ZqlvfG6DozGOh+3cp/L5V/f8Z7oeQ/XOr2r5hWrmuSzyxDD4M67z55BPv1cY3AQc0PB6/5RmVhWOYRsEjmMbBI5jK0S/C8/fBRZKOlDSM4HjgDV9zoNZJxzDNggcxzYIHMdWiL5224iICUmnAV8nm1bmooi4o83DzNj8UmE+t5KbZQwPxLl3wOdfMl26FteV7vxmULX8QjXz3HNDHse94s8hB0VE0XkwMzMzM6uEfnfbMDMzMzOrLBeezczMzMxyKm3hWdJRku6WtF7S8ibbd5d0edp+k6TR/udydnKc27sk3SnpdknXSCpi/sxZmencGvb7U0khaWCmxBnkmM0jx/m/TNKtkiYkHVNEHntpkL/XUL34rlo8Dnr8lEHVYriXcnwWyyT9VNJt6fE/ishnaUVE6R5kHf9/BPwm8Ezg+8BBU/b5S+Af0/PjgMuLzncXz+0VwLPT8/85SOeW9tsLuA64EVhSdL77+HetZMx28fxHgRcBlwDHFJ3nAs6/kt/rNs6vNPFdtXgc9Pgpw6NqMVyCz2IZ8PGi81rWR1lrnncsuRkRvwTqS242WgqsSs+vBI6UpD7mcbZmPLeIuDYinkgvbySbu7IK8vzdAN4PfBD4RT8z12ODHLN55InrDRFxO/DfRWSwxwb5ew3Vi++qxeOgx08ZVC2Geynv/2proayF52ZLbi5otU9ETADbgH37krvO5Dm3RicDX+1pjrpnxnOTdAhwQES0Xoy+mgY5ZvNoN64HzSB/r6F68V21eBz0+CmDqsVwL+WNtz9N3YSulHRAk+1Dq3TLc9vTJL0NWAK8vOi8dIOkZwDnkzUHmQ2lQfteW385fqxP/hW4LCKelPTnZDXyryw4T6VR1prnPEtu7thH0q7Ac4CH+5K7zuRaTlTSq4D3AG+MiCf7lLdOzXRuewEvBGqSNgCHA2sGZNDgIMdsHsO+TO4gf6+hevFdtXgc9Pgpg6rFcC/N+FlExMMNMfYZ4Pf6lLdKKGvhOc+Sm2uAE9PzY4BvRerlXnIznpukg4FPkV0gtxSQx9ma9twiYltE7BcRoxExStZv740RcXMx2e2qQY7ZPIZ9mdxB/l5D9eK7avE46PFTBlWL4V7KE2/zG16+Ebirj/krv6JHLLZ6AEcDPyQbEfqelPY+sgsHwLOAzwPrge8Av1l0nrt4bt8EHgRuS481Ree5W+c2Zd8aAzLbRs6/a2Vjtkvn//tkfeseJ6vNuaPoPPf5/Cv7vc55fqWK76rF46DHTxkeVYvhgj+L/w+4g2wmjmuB3yk6z2V6eHnuCpM0BvxzRHjUtZVWGq1+EfAm4J6IOLTgLJlNS9J7gRdExNuKzouZlY8HDJpZr70UeDWwf0Q8XnRmzMysM2kBmR8Du0U2M8lQKWufZzMbHM8HNrjgbGZmg8CF55KSdKakTZIeS0toHilpD0kXS9oq6U6yPntmpdEkbk8mG6n9B5K2S/p7SWOSNkr6G0lbJG2W9CZJR0v6oaRHJJ1d9LnYcGh2rZ2yfTdJl0n6gqRnSnqGpOWSfiTpYUlXSNon7btK0nh6vkBSSDo1vf6tFNv+v2td0aKc8N40L/PlKf1WSS+e5hiHS/oPSY9K+n7qDlrfVpP0fkn/no71DUn7pc3XpZ+Ppmv7H/TwVEvHX+ISkrQIOA34/YjYC3gtsAE4B/it9HgtT48KNitci7itAX8B3BARcyLinLT7r5MNzlkA/B3waeBtZNMh/SHwvyUd2N8zsGEzzbW2vn0P4F+AJ4FjI1uN7Z1k/fdfDjwP2Ap8Iv3Kt4Gx9PzlwL3Ayxpe/1tElGFFQ6u4GWJ3KdnAx32AzwH/Imm3JsdYAHwF+EDa993AFyTNa9jtrcBJwHPJlvJ+d0qvx/XcdG2/oXtnV34uPJfTU8DuwEGSdotsGdkfAccC50bEIxFxP/DRQnNpNlmruG3mV2Sx/CuypWH3A1ZGxGMRcQdwJ9CytsSsS6aL2V8DvkY2G8FJEfFUSv8LstkJNkY2D+57gWOUzQv8beClqXb5ZcA/AEek33t52m7WDdPF7i0RcWW6vp5PVlFxeJNjvA24OiKujoj/joi1wM1kM3HU/VNE/DAifg5cAbykZ2dUIS48l1BErAfOILsob5G0WtLzyGo5GpfUvK+A7Jk1NU3cNvNwQ2Hk5+nngw3bfw7M6UlGzZIZYvZw4EXAipg8LdXzgS+lZu5Hyea/fQoYSYWXx8kKGH8IfBn4SaoldOHZumaG2L2/Yb//JpuSsdm1+PnAn9VjOcXzS4HGOZ4faHj+BL4uAy48l1ZEfC4iXkoW3AF8ENjM5FWBfqOIvJm10iJuzUprmpj9Btlct9dIGmn4lfuB10XE3IbHsyKivkLbt8kW2HhmSvs2WRe7vcnmZzbrimlid0c5IbWC7A/8pMkh7gc+OyWW94yIFXnevsPsV5oLzyUkaZGkV0raHfgFWS3cf5M1mZwlaW9J+5P1vTMrhWni1qyUZorZiPgHsj6j1zQMlPpH4FxJz0/HmCdpacNhv03WF7U+oKqWXl/f0Npi1pEZYvf3JP1J6kp0Blmf/RubHOafgT+S9FpJu0h6VhrQnWftiJ+m9/vNzs+melx4LqfdgRXAQ2RNJs8FzgL+nqyrxo/JakU+W1QGzZpoFbdmZTVjzEbE+8kGDX4zzaqxkmwp429IeoysUHJYw698G9iLpwvP1wPPbnht1g3Txe5VwJvJBrOeAPxJ6v+MpK/WZzNKY6eWAmeTFYbvB/6aHGXDiHgCOBf499Tlo1mf6oHlFQbNzMzMBoC8OmZfuObZzMzMzCwnF57NzMzMzHJytw0zMzMzs5xc82xmZmZmlpMLz2ZmZmZmOe1adAams99++8Xo6GjR2ZiVxx9/nD333LPobBSmX+d/yy23PBQR83r+Rh3Yb7/9Yt68eaWMhzLHaVnz1ot8VSWOp16Py/Y3cn6m18v8VDWGy6BscdKJqp9L3jgudeF5dHSUm2++uehszEqtVmNsbKzobBSmX+cvqfRLlI+OjvKhD32olPFQ5jgta956ka+qxPHU63HZ/kbOz/R6mZ+qxnAZlC1OOlH1c8kbx+62YWZmZmaWkwvPZmZmZmY5ufBsZmZmZpZTqfs8D7rR5V9puW3Ditf3MSc2bOqxN754gmVT4tCxZ93Q6vrm+DIbLoN4LXDNs5mZmZlZTq55Nhtg07VumJmZWftceDYzMzOzvqpydw532zAzMzMzy8k1z2ZmZmY2a9MNQh9Ernk2M6sISRdJ2iLpBw1p75W0SdJt6XF0w7azJK2XdLek1zakH5XS1kta3u/zMDOrMtc8m9kkve6H5ikaO3Ix8HHgkinpH46IDzUmSDoIOA74XeB5wDcl/Xba/Ang1cBG4LuS1kTEnb3MuJnZoHDh2cysIiLiOkmjOXdfCqyOiCeBH0taDxyatq2PiHsBJK1O+7rwbGaWgwvPZmbVd5qktwM3A+MRsRVYANzYsM/GlAZw/5T0w5odVNIpwCkAIyMj1Gq1Sdu3b9++U1rd+OKJpumt9u+G6fJTBOfHbDC58GxmVm2fBN4PRPp5HvCObhw4Ii4ALgBYsmRJjI2NTdpeq9WYmlbXatDQhuOb798N0+WnCM6P2WBy4dnMrMIi4sH6c0mfBr6cXm4CDmjYdf+UxjTpZmY2A8+2YWZWYZLmN7z8Y6A+E8ca4DhJu0s6EFgIfAf4LrBQ0oGSnkk2qHBNP/NsNpWkZ0n6jqTvS7pD0t+n9AMl3ZRmhrk8xSwpri9P6Te1MRbArGMuPJuZVYSky4AbgEWSNko6GfgHSesk3Q68AvhfABFxB3AF2UDArwGnRsRTETEBnAZ8HbgLuCLta1akJ4FXRsSLgZcAR0k6HPgg2WwyLwC2Aien/U8Gtqb0D6f9zPqio24bkuYCnwFeSNbf7h3A3cDlwCiwATg2IrZKErASOBp4AlgWEbd28v5mZsMkIt7SJPnCafY/Fzi3SfrVwNVdzJpZRyIigO3p5W7pEcArgbem9FXAe8n6+S9NzwGuBD4uSek4Zj3VaZ/nlcDXIuKY1JTybOBs4JqIWJEm318OnAm8jqzZcCHZyO5P0mKEt5mZmQ0XSbsAtwAvIJuL/EfAo6m1BCbPGLOANGtMRExI2gbsCzw05ZjTzhhTBoMwC0p9dp2RPVrPtJNXFT6LWReeJT0HeBmwDCAifgn8UtJSYCzttgqokRWelwKXpLvCGyXNlTQ/IjbPOvdmZmY2ECLiKeAlqVX7S8DvdOGY084YUwaDMAvKsobluc9b11m9bC9n5OmWTvo8Hwj8FPgnSd+T9BlJewIjDQXiB4CR9HzHXWLSeAdpZmZmRkQ8ClwL/AEwV1K9NNY4M8yO2WTS9ucAD/c5qzakOrk92BU4BHhnRNwkaSVZF40dIiIktdX/qApNLHnkaYaZrmmjquddNwjNUGZm1h+S5gG/iohHJe1Btnz8B8kK0ccAq4ETgavSr6xJr29I27/l/s7WL50UnjcCGyPipvT6SrLC84P17hhpCqUtaft0c47uUIUmljzyNMO0WkQAqtFsMZ1BaIayzoxOE99mZlPMB1alfs/PIJsF5suS7gRWS/oA8D2eHiB7IfDZtOz8I2RTLpr1xawLzxHxgKT7JS2KiLuBI8mmRLqT7G5wBTvfJZ4maTXZQMFt7u9sZmZmEXE7cHCT9HuBQ5uk/wL4sz5kzWwnnc628U7g0jTTxr3ASaQ7xjT/6H3AsWnfq8mmqVtPNlXdSR2+t5klruU1MzPrj44KzxFxG7CkyaYjm+wbwKmdvJ+ZmZmZDa5WlUEbVry+zzlpzSsMmpmZmZnl1Gm3DTOzrmmscRhfPLFjUG2ZahzMzGy4uebZhpqkAyRdK+lOSXdIOj2l7yNpraR70s+9U7okfVTSekm3Szqk2DMwMzOzfnLh2YbdBDAeEQcBhwOnSjqIbNrFayJiIXANT89h3rjM/Clky8ybmZnZkHC3DRtqabrEzen5Y5LuIlv50svMT1GFQRxmZma95ppns0TSKNk8ozfhZebNzMysCdc8mwGS5gBfAM6IiJ9J2rGtG8vM93q58umWep/OyB6z/926j116VdP08cUdHXZS3sq01LuXnjczG24uPNvQk7QbWcH50oj4Ykru6jLzc+bM6ely5dMt9T6d8cUTnLeunJeBxryVabl6Lz1vZjbcyvlf06xPlFUxXwjcFRHnN2xag5eZLw33tzYzK55Xs8248GzD7gjgBGCdpNtS2tlkhWYvM282A/8zNbNh48KzDbWIuB5Qi81eZt7MzMwm8WwbZmZmZmY5ufBsZmZmZpaTu22YmVWEpIuANwBbIuKFKW0f4HJgFNgAHBsRW9Ng2JVkffSfAJZFxK3pd04E/jYd9gMRsaqf52Fm5eaxDNNzzbOZWXVcDBw1Ja2tpeRTYfscstliDgXOkbR3z3NuZjYgXHg2M6uIiLgOeGRK8lKyJeRJP9/UkH5JZG4E5qY5y18LrI2IRyJiK7CWnQvkZmbWgrttmJlVW7tLyedeYn7qSplTV1bcvn0744ufaiuzvVydsWyrPzo/VhbuhtFdLjybmQ2I2SwlP8PxJq2UOXVlxVqtxnnXP97WMXu5WmTZVn90fvKTdABwCdnNXwAXRMTK2fTpN+s1d9swM6u2B1N3DHIuJZ9riXmzPpsAxiPiIOBw4FRJB9Fmn36zfui48CxpF0nfk/Tl9PpASTdJWi/pcknPTOm7p9fr0/bRTt/bzMx2LCUPOy8l/3ZlDufppeS/DrxG0t5poOBrUppZYSJic73mOCIeA+4i607Ubp9+s57rRs3z6WRBXvdB4MMR8QJgK3BySj8Z2JrSP5z2MzOznCRdBtwALJK0MS0fvwJ4taR7gFel15AtJX8v2VLynwb+EiAiHgHeD3w3Pd6X0sxKIVWuHQzcRPt9+s16rqM+z5L2B14PnAu8K/VBeiXw1rTLKuC9ZM0pS9NzgCuBj0tSWu7YzMxmEBFvabGpraXkI+Ii4KIuZs2sKyTNAb4AnBERP8uKFZnZ9OmfadBrGfRjIOf44omeHr9uZI/evVeZ/nadDhj8CPA3wF7p9b7AoxFR/+Qa7wR33CVGxISkbWn/hxoPWIVAzyPPl2G6AKvqedd5VLeZmbVD0m5kBedLI+KLKflBSfMjYnPOPv2TzDTotQz6MZBzWZ9m2xhfPMF563ozF0UvBxu3a9ZnKKm+ytUtksa6laEqBHoeeb4M0wVzmYJkNso8qtvMzMoltVxfCNwVEec3bKr36V/Bzn36T5O0mmzBn20N3TvMeqqT24MjgDdKOhp4FvBrZNPGzJW0a6p9brwTrN8lbpS0K/Ac4OEO3t/MzMwGwxHACcA6SbeltLPJCs1XpP799wHHpm1Xk01Tt55sqrqT+ptdG2azLjxHxFnAWQCp5vndEXG8pM8DxwCr2fku8USywS7HAN9yf2czMzOLiOsBtdjcVp9+Gy7TLQCzYcXre/KevZjn+UyywYPryfo0X5jSLwT2Tenv4um5Gs3MzMzMKqErvbojogbU0vN7gUOb7PML4M+68X5mZtC6xqFXtQ1mZmZDvzx3EdX9ZmZmZpbfdOW1fhv6wrOZmZlZ1ZWpcDnoetHn2czMzMxsILnm2cwGjvtCm5lZr7jwXFL+529mZmZWPi48m1WI+7SZmQ03/x8onvs8m5mZmZnl5MKzmZmZmVlO7rZhZkNjNvO6T/2d8cUTLFv+FY8/MDMbUq55NjMzMzPLyYVnG2qSLpK0RdIPGtL2kbRW0j3p594pXZI+Kmm9pNslHVJczs3MzKwILjzbsLsYOGpK2nLgmohYCFyTXgO8DliYHqcAn+xTHs3MzKwkXHi2oRYR1wGPTEleCqxKz1cBb2pIvyQyNwJzJc3vT07NzMysDDxg0GxnIxGxOT1/ABhJzxcA9zfstzGlbWYKSaeQ1U4zMjLC9u3bqdVqHWdsfPFEx8doNLJH94/ZLf3OW6u/z9Q81PPVjb+nmZlVjwvPZtOIiJAUs/i9C4ALAJYsWRJz5sxhbGys4/ws6/Lk+OOLJzhvXTkvA/3O24bjx5qmT/3M6/lqtb+ZmQ22cv7XNCvWg5LmR8Tm1C1jS0rfBBzQsN/+Kc0GQNVX7ZK0AXgMeAqYiIglkvYBLgdGgQ3AsRGxVZKAlcDRwBPAsoi4tYh8m5lVjQvP02j1z7Td+V2r/k95CK0BTgRWpJ9XNaSfJmk1cBiwraF7h1kZvCIiHmp4XR/8ukLS8vT6TCYPfj2MbPDrYf3OrJlZFXnAoA01SZcBNwCLJG2UdDJZofnVku4BXpVeA1wN3AusBz4N/GUBWTZrhwe/WmV46lCrilkXniUdIOlaSXdKukPS6SndgW6VERFviYj5EbFbROwfERdGxMMRcWRELIyIV0XEI2nfiIhTI+LOJA69AAALVElEQVS3ImJxRNxcdP7NGgTwDUm3pAGr0P7gV7MiXYynDrUK6KTbxgQwHhG3StoLuEXSWmAZbiY0M+u3l0bEJknPBdZK+s/GjbMZ/Dp11pipM4xs376d8cVPtZXJXs5S0q1ZbbrF+WlPRFwnaXRK8lJgLD1fBdTIyhQ7Wk+AGyXNrY9V6U9ubZjNuvCcAnRzev6YpLvIai4c6GZmfRYRm9LPLZK+BBxKh4Nfp84aM3XGmFqtxnnXP95WPns5S0mtVuvKrDbd4vx0RcdTh5p1W1cGDKY7xYOBm+gw0Geq6ei22cwjmydPjXf43Zyrtsy1Bo3KXsNhNkgk7Qk8I1Vk7Am8BngfHvxqA6QXrSdl0O7/y7LOzQ/lWzugV3/vjgvPkuYAXwDOiIifZTMgZWYT6DPVdMxW6xkv2v8I8tScNN7hd3Nu3qrMLVvRGg6zqhrh/2/vXmPtqMowjv+flNJqAGkpIYRLLwatTYqCiBcUUKQUEkGhMa2BtFhCgmI01A9gE0W/iJqgJpogAWIl3CvGmqCkQtFouAq9UEjLoaIWkGKxKGrQ1tcPs4qTk3OZffaeNbP3eX7J5Mys2bP3u2a/s/Y6c4WfpPb3AODWiPiFpEeBO9OFsH8APplefw/FbeqGKG5Vd3H+kM0qqfXoSRuM9nvZy35LLm17dkBdfaauaihpKkXH+ZaIuDsV+x65NerV7fPMbHBExA7gnSOU7wbOGKE8gM9mCM2sWz56Yq0z4c5zusn+jcDTEXFtaZYTvUXc2TYzs36Qbh16OjBL0k7gKxR9CR89sVbpZs/zKcBFwBZJG1PZl3CiN8IPYjEzs34WEctGmeWjJ9Yq3dxt4zeARpk90Inuvblm5nbAzGxy8hMGzczMzMwqcufZzMzMzKwid57NzMzMzCpy59nMzMzMrCJ3ns3MzMzMKnLn2czMzMysovY8Q9GyGuu+0L7VlpmZWR5bnn+VFX5WQ1/xnmczMzMzs4q859nMzMzMBk5dD7PynmczMzMzs4q857mHyv/hrFq4t2/PYfJjh83MzMxG5s6zWQuNdUGnWT/zxcpm1u982oaZmZmZWUXe82xmZmZWo7GOuKxamDEQ6wnveTYzMzMzq8idZzMzMzOzinzahplZD/mCODOzwebOs1XmW9iZmZmNzndKmhwGrvPsxDUz60/+B93M+kH2zrOkxcB3gSnADRFxTe4YzLrhHLZB4Dy2QdBUHntH3eSWtfMsaQrwfeBMYCfwqKR1EfFUzjjMJso5bIOg3/LYe6RtJL3MY+eYdSL3nueTgaGI2AEg6XbgPKCVDbZVM1Kjs2rhXk7v4PXjaVED1rMc9p4La9BAtMXDt6FVC/eyYoLb1WhtTKfbaa/epw6drJ8WtbljGYg8tv6jiMj3YdISYHFEXJKmLwLeGxGXl15zKXBpmnw7sC1bgL01C/hL00E0KFf9Z0fE4Rk+B6iWw6l8eB7vpp350OY8bWtsdcTVL3k8vD1u23fkeMZWZzxZcxgGqk/RtjzpRr/XpVIet+6CwYi4Hri+6Ti6JemxiDip6TiaMtnrPzyP27o+2hoXtDe2tsZVh/Ha47atC8cztrbFk0M/9CkG6XsZpLqMJfdDUp4HjilNH53KzPqFc9gGgfPYBoHz2BqRu/P8KHCcpLmSDgSWAusyx2DWDeewDQLnsQ0C57E1IutpGxGxV9LlwL0Ut5W5KSK25owho1YfJspgIOvfRQ63dX20NS5ob2xtjauyHrbFbVsXjmdsbYunKwPUpxik72WQ6jKqrBcMmpmZmZn1s9ynbZiZmZmZ9S13ns3MzMzMKnLneQIkLZa0TdKQpCtHmD9N0h1p/sOS5qTyOZL+JWljGq7LHXsvVKj/qZIel7Q33YezPG+5pGfSsDxf1PWYaC6keVel8m2SzmogtiskPSVps6T7JM0uzdtXytOeXoBTIa4Vkl4uff4lpXm15k+F2L5dimu7pD2lebWtszYab11l+PybJO2S9GSpbKak9Sk/1kuakTGeYyRtSNvUVkmfbzImSdMlPSJpU4rnq6l8bmqLhlLbdGCOeKxS+3JsyqEnUrt8ThNxVlGhLrPT78pmSQ9IOrqJOGsTER46GCguSngWmAccCGwCFgx7zWeA69L4UuCOND4HeLLpOmSo/xzgeOBHwJJS+UxgR/o7I43PaLpODeXCgvT6acDc9D5TMsf2YeDNafyy/bGl6dcaXGcrgO+NsGyt+VMltmGv/xzFBUq1rrM2Dp2uq5piOBU4sdymAt8ErkzjVwLfyBjPkcCJafxgYHvazhuJCRBwUBqfCjwMvA+4E1iayq8DLms6nybDULHtu37/95Fy57mm4+6iLncBy9P4R4Cbm467l4P3PHfujceBRsS/gf2PAy07D1iTxtcCZ0hSxhjrNG79I+K5iNgM/HfYsmcB6yPilYj4K7AeWJwj6Jp0kwvnAbdHxOsR8XtgKL1fttgiYkNE/DNNPkRxj9S6VVlno6k7fzqNbRlwWw8/v5908z32RET8GnhlWHF5e1sDfDxjPC9GxONp/O/A08BRTcUUhdfS5NQ0BEVHZm3ueKzSNhPAIWn8LcALGePrRJW6LADuT+MbRpjf19x57txRwJ9K0ztT2YiviYi9wKvAYWne3HRI5leSPlR3sDWoUv86lm2jbnKh7nXR6fuvBH5emp4u6TFJD0nq5Y9r1bguSIf71kra/xCE1qyzdIrLXP7/4wD1rbM2auu2fEREvJjG/wwc0UQQ6fSsEyj29jYWk6QpkjYCuyj+2XwW2JPaImjP9zYZVNlmrgYulLQTuIfi6FYbVanLJuD8NP4J4GBJhzEg3HnO60Xg2Ig4AbgCuFXSIeMsY1YrSRcCJwHfKhXPjuIRq58CviPprRlD+hkwJyKOp/jBXzPO65uwFFgbEftKZU2uMxsmiuPF2e/FKukg4MfAFyLib03GFBH7IuJdFEeVTgbm5/psm5BlwA8j4mjgHOBmSf3aT/sicJqkJ4DTKJ78uG/sRfpHv34pTaryONA3XiPpAIrDL7vTIfrdABHxO4q9AG+rPeLe6uZxqIP2KNUJ50LFZeuODUkfBVYD50bE6/vLI+L59HcH8ADFXrQscUXE7lIsNwDvrrps3bGVLGXYKRs1rrM2auu2/JKkIwHS3105P1zSVIqO8y0RcXcbYgKIiD0Uh87fDxya2iJoz/c2GVTZZlZSnJNORDwITAdmZYmuM1Xa8Rci4vy0s3B1KtvDgHDnuXNVHge6Dth/J4AlwP0REZIOlzQFQNI84DiKi576STePQ70XWCRpRrrifFEq61cTzoVUvlTF3TjmUuTCIzljk3QC8AOKjvOuUvkMSdPS+CzgFOCpjHEdWZo8l+LcUag/fyrltqT5FBcsPlgqq3OdtVFbH4tc3t6WAz/N9cHpWoYbgacj4tqmY0q/N4em8TcBZ1JsSxso2qKs8VilbeaPwBkAkt5B0Xl+OWuU1VRpx2eV9ppfBdyUOcZ6NX3FYj8OFIdTtlPsOV6dyr5G0QmBIuHvorgI7BFgXiq/ANgKbAQeBz7WdF1qqv97KM6B+gfFXtatpWU/ndbLEHBx03VpKhfSvNVpuW3A2Q3E9kvgpZSPG4F1qfwDwBaKc9a2ACszx/X1tJ1sovihn58rf8aLLU1fDVwzbLla11kbh5HWVebPv43iVLj/pPZmJcX1BPcBz6T8npkxng9SnJKxubRNndNUTBR3PHoixfMk8OVUPi+1RUOpbZrWdC5NlqFC27cA+G1qRzYCi5qOuYu6LEk5v53iCOJA5Zkfz21mZmZmVpFP2zAzMzMzq8idZzMzMzOzitx5NjMzMzOryJ1nMzMzM7OK3Hk2MzMzM6vInWczMzMzs4rceTYzMzMzq+h/3A9SswgS99wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x864 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12, 12))\n",
    "features_raw.hist(ax = fig.add_subplot(111), bins = 20)\n",
    "fig.show()\n",
    "fig.savefig(\"Distribution.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Observation\n",
    "`IQR`, `meanfun`, `sd` have 2 peaks. It may represent male and female. So these 3 features should be important."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Skew\n",
    "`dfrange`, `kurt` and `skew` have some kind of skewness. So apply log transform on them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "skewed = ['dfrange', 'kurt', 'skew']\n",
    "features_raw[skewed] = data[skewed].apply(lambda x: np.log(x + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jachinshen/Projects/python/Udacity/lib/python3.6/site-packages/IPython/core/interactiveshell.py:2910: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "/home/jachinshen/Projects/python/Udacity/lib/python3.6/site-packages/matplotlib/figure.py:459: UserWarning: matplotlib is currently using a non-GUI backend, so cannot show the figure\n",
      "  \"matplotlib is currently using a non-GUI backend, \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (12, 12))\n",
    "features_raw.hist(ax = fig.add_subplot(111), bins = 20)\n",
    "fig.show()\n",
    "fig.savefig(\"Distribution_log.svg\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Normalization\n",
    "The large range can reach to 0~1000, while the small range can be limited to 0~0.2, so features need to be normalized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "numerical = features_raw.columns\n",
    "features_raw[numerical] = scaler.fit_transform(data[numerical])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Store Normalization Parameters\n",
    "If we want to predict voice in real life, these parameters are necessary to normalize the original features of newly collected voice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_min = scaler.data_min_\n",
    "features_max = scaler.data_max_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Encode\n",
    "- Features are numbers. So one-hot encoding is not necessary.\n",
    "- `Label` has only 2 options, so encode it in 0/1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = features_raw\n",
    "label = label_raw.apply(lambda x: 1 if x == 'male' else 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Split and Shuffle Dataset\n",
    "The first half is male and the last half is female, so shuffling is necessary. Otherwise, the test set only contains female samples. Thus, the test set cannot actually test the performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set has 2027 samples.\n",
      "Validation set has 507 samples.\n",
      "Testing set has 634 samples.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    features, label, test_size = 0.2,\n",
    "    random_state = 549325, stratify = label)\n",
    "X_train, X_val, y_train, y_val = train_test_split(\n",
    "    X_train, y_train, test_size = 0.2,\n",
    "    random_state = 983275, stratify = y_train)\n",
    "\n",
    "print(\"Training set has {} samples.\".format(X_train.shape[0]))\n",
    "print(\"Validation set has {} samples.\".format(X_val.shape[0]))\n",
    "print(\"Testing set has {} samples.\".format(X_test.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Save Preprocessed Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('train_set.pkl', 'wb') as fid:\n",
    "    pickle.dump((X_train, y_train), fid,2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('val_set.pkl', 'wb') as fid:\n",
    "    pickle.dump((X_val, y_val), fid,2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('test_set.pkl', 'wb') as fid:\n",
    "    pickle.dump((X_test, y_test), fid,2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('all_feature_min_max.pkl', 'wb') as fid:\n",
    "    pickle.dump((features_min, features_max), fid,2) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
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 },
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